diff --git a/configs/experiment.ini b/configs/experiment.ini deleted file mode 100644 index 5386039..0000000 --- a/configs/experiment.ini +++ /dev/null @@ -1,21 +0,0 @@ -[experiment] -name = experiment - -[task] -task_name = agnews,cr,mr,sst-5,sst2,subj,trec -ds_path = data_sets/cls -steps = 12 - -[optimizer] -name = evopromptga,evopromptde -init_population = 10 -meta_prompt_path = templates/evoprompt_ga_template.txt,templates/evoprompt_de_template.txt - -[meta_llm] -name = meta-llama/Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct - -[evaluator_llm] -name = meta-llama/Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct - -[downstream_llm] -name = meta-llama/Meta-Llama-3-70B-Instruct diff --git a/configs/experiment_eval.ini b/configs/experiment_eval.ini deleted file mode 100644 index c0a3e7f..0000000 --- a/configs/experiment_eval.ini +++ /dev/null @@ -1,15 +0,0 @@ -[experiment] -name = experiment_eval - -[target_experiment] -name = experiment - -[task] -task_name = agnews,cr,mr,sst-5,sst2,subj,trec -optimizer = evopromptde, evopromptga -llm = Meta-Llama-3-70B-Instruct, Meta_Llama-3-8B-Instruct -subsample_seed = 42 -n_samples = 200 - -[downstream_llm] -name = meta-llama/Meta-Llama-3-70B-Instruct diff --git a/configs/experiment_eval_task_descr.ini b/configs/experiment_eval_task_descr.ini deleted file mode 100644 index 1eabc3a..0000000 --- a/configs/experiment_eval_task_descr.ini +++ /dev/null @@ -1,13 +0,0 @@ -[experiment] -name = evaluation_task_descr - -[target_experiment] -name = experiment_task_descr - -[task] -task_name = agnews,cr,mr,sst-5,sst2,subj,trec -subsample_seed = 42 -n_samples = 200 - -[downstream_llm] -name = meta-llama/Meta-Llama-3-70B-Instruct diff --git a/configs/experiment_gpt.ini b/configs/experiment_gpt.ini deleted file mode 100644 index cee235a..0000000 --- a/configs/experiment_gpt.ini +++ /dev/null @@ -1,15 +0,0 @@ -[experiment] -name = experiment_gpt - -[target_experiment] -name = experiment - -[task] -task_name = agnews,cr,sst-5,subj -optimizer = evopromptde -llm = Meta-Llama-3-70B-Instruct -subsample_seed = 42 -n_samples = 200 - -[downstream_llm] -name = gpt-4o-2024-05-13 diff --git a/configs/experiment_initial_prompts.ini b/configs/experiment_initial_prompts.ini deleted file mode 100644 index cef685f..0000000 --- a/configs/experiment_initial_prompts.ini +++ /dev/null @@ -1,11 +0,0 @@ -[experiment] -name = experiment_initial_prompts - -[task] -task_name = agnews,cr,mr,sst-5,sst2,subj,trec -subsample_seed = 42 -n_prompts = 3 -n_samples = 200 - -[downstream_llm] -name = meta-llama/Meta-Llama-3-70B-Instruct diff --git a/configs/experiment_task_descr.ini b/configs/experiment_task_descr.ini deleted file mode 100644 index a06cbd4..0000000 --- a/configs/experiment_task_descr.ini +++ /dev/null @@ -1,21 +0,0 @@ -[experiment] -name = experiment_task_descr - -[task] -task_name = agnews,cr,mr,sst-5,sst2,subj,trec -ds_path = data_sets/cls -steps = 12 - -[optimizer] -name = evopromptga,evopromptde -init_population = 10 -meta_prompt_path = templates/evoprompt_ga_template_task_desc.txt,templates/evoprompt_de_template_task_desc.txt - -[meta_llm] -name = meta-llama/Meta-Llama-3-8B-Instruct - -[evaluator_llm] -name = meta-llama/Meta-Llama-3-8B-Instruct - -[downstream_llm] -name = meta-llama/Meta-Llama-3-70B-Instruct diff --git a/docs/release-notes.md b/docs/release-notes.md index bef46ad..8977cf8 100644 --- a/docs/release-notes.md +++ b/docs/release-notes.md @@ -1,7 +1,24 @@ # Release Notes -## Release v1.0.0 +## Release v1.1.0 +### What's changed +#### Added features +* Enable reading tasks from a pandas dataframe + +#### Further Changes: +* deleted experiment files from the repo folders (logs, configs, etc.) +* improved opros meta-prompt +* added support for python versions from 3.9 onwards (previously 3.11) + +## Release v1.0.1 +### What's changed +#### Added features +- + +#### Further Changes: +* fixed release notes +## Release v1.0.0 ### What's changed #### Added Features: * exemplar selection module, classes for exemplar selection (Random and RandomSearch) @@ -79,3 +96,4 @@ * Added experiment results and evaluation notebooks **Full Changelog**: [here](https://github.com/finitearth/promptolution/commits/0.1.0) + diff --git a/logs/experiment-initial-prompts/best_scores.csv b/logs/experiment-initial-prompts/best_scores.csv deleted file mode 100644 index 9d54c36..0000000 --- a/logs/experiment-initial-prompts/best_scores.csv +++ /dev/null @@ -1,43 +0,0 @@ -task,downstream_llm,random_seed,prompt,test_score -agnews,gpt-4o-2024-05-13,42,"Your responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.",0.86 -agnews,gpt-4o-2024-05-13,42,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.86 -agnews,gpt-4o-2024-05-13,42,"Give the main topic of the news article and then choose from World, Sports, Tech and Business.",0.845 -cr,gpt-4o-2024-05-13,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.96 -cr,gpt-4o-2024-05-13,42,"Your task is to classify the comment ""positive"" or ""negative"".",0.585 -cr,gpt-4o-2024-05-13,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.925 -mr,gpt-4o-2024-05-13,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.925 -mr,gpt-4o-2024-05-13,42,"Your task is to classify the comment ""positive"" or ""negative"".",0.345 -mr,gpt-4o-2024-05-13,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -sst-5,gpt-4o-2024-05-13,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.545 -sst-5,gpt-4o-2024-05-13,42,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.425 -sst-5,gpt-4o-2024-05-13,42,"Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.",0.165 -sst2,gpt-4o-2024-05-13,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -sst2,gpt-4o-2024-05-13,42,"Your task is to classify the comment ""positive"" or ""negative"".",0.4 -sst2,gpt-4o-2024-05-13,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.89 -subj,gpt-4o-2024-05-13,42,"classify each sentence as either ""objective"" or ""subjective"".",0.665 -subj,gpt-4o-2024-05-13,42,"Your task is to classify the comment ""subjective"" or ""objective"".",0.435 -subj,gpt-4o-2024-05-13,42,evaluate each sentence as either objective or subjective.,0.615 -trec,gpt-4o-2024-05-13,42,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.84 -trec,gpt-4o-2024-05-13,42,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.705 -trec,gpt-4o-2024-05-13,42,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.525 -agnews,meta-llama/Meta-Llama-3-70B-Instruct,42,"Your responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.",0.885 -agnews,meta-llama/Meta-Llama-3-70B-Instruct,42,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.87 -agnews,meta-llama/Meta-Llama-3-70B-Instruct,42,"Give the main topic of the news article and then choose from World, Sports, Tech and Business.",0.845 -cr,meta-llama/Meta-Llama-3-70B-Instruct,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -cr,meta-llama/Meta-Llama-3-70B-Instruct,42,"Your task is to classify the comment ""positive"" or ""negative"".",0.93 -cr,meta-llama/Meta-Llama-3-70B-Instruct,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.915 -mr,meta-llama/Meta-Llama-3-70B-Instruct,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.925 -mr,meta-llama/Meta-Llama-3-70B-Instruct,42,"Your task is to classify the comment ""positive"" or ""negative"".",0.92 -mr,meta-llama/Meta-Llama-3-70B-Instruct,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.845 -sst-5,meta-llama/Meta-Llama-3-70B-Instruct,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.62 -sst-5,meta-llama/Meta-Llama-3-70B-Instruct,42,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.535 -sst-5,meta-llama/Meta-Llama-3-70B-Instruct,42,"Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.",0.04 -sst2,meta-llama/Meta-Llama-3-70B-Instruct,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.96 -sst2,meta-llama/Meta-Llama-3-70B-Instruct,42,"Your task is to classify the comment ""positive"" or ""negative"".",0.945 -sst2,meta-llama/Meta-Llama-3-70B-Instruct,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.89 -subj,meta-llama/Meta-Llama-3-70B-Instruct,42,"classify each sentence as either ""objective"" or ""subjective"".",0.465 -subj,meta-llama/Meta-Llama-3-70B-Instruct,42,"Your task is to classify the comment ""subjective"" or ""objective"".",0.61 -subj,meta-llama/Meta-Llama-3-70B-Instruct,42,evaluate each sentence as either objective or subjective.,0.545 -trec,meta-llama/Meta-Llama-3-70B-Instruct,42,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.67 -trec,meta-llama/Meta-Llama-3-70B-Instruct,42,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.595 -trec,meta-llama/Meta-Llama-3-70B-Instruct,42,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.33 diff --git a/logs/experiment-task-descr/agnews_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment-task-descr/agnews_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 1f5818b..0000000 --- a/logs/experiment-task-descr/agnews_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -1,"Your task is to classify the news item as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -1,"Your responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.",0.9 -1,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -1,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -1,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -1,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -1,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -1,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -1,"Give the main topic of the news article and then choose from World, Sports, Tech and Business.",0.75 -2,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -2,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -2,"Your responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.",0.9 -2,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -2,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -2,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -2,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -2,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -2,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -2,"Give the main topic of the news article and then choose from World, Sports, Tech and Business.",0.75 -3,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -3,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -3,"Your responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.",0.9 -3,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -3,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -3,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -3,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -3,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -3,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -3,"Give the main topic of the news article and then choose from World, Sports, Tech and Business.",0.75 -4,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -4,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -4,"Your responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.",0.9 -4,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -4,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -4,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -4,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -4,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -4,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -4,"Give the main topic of the news article and then choose from World, Sports, Tech and Business.",0.75 -5,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -5,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -5,"Consider the central theme of the reports and classify it as ""World"", ""Sports"", ""Tech"", or ""Business"" based on its idea.",0.95 -5,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -5,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -5,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -5,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -5,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -5,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -5,"Determine the category for the given news based on the main topic, and choose from World, Sports, Business, or Tech.",0.8 -6,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -6,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -6,"Consider the central theme of the reports and classify it as ""World"", ""Sports"", ""Tech"", or ""Business"" based on its idea.",0.95 -6,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -6,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -6,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -6,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -6,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -6,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -6,"Determine the category for the given news based on the main topic, and choose from World, Sports, Business, or Tech.",0.8 -7,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -7,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -7,"Consider the central theme of the reports and classify it as ""World"", ""Sports"", ""Tech"", or ""Business"" based on its idea.",0.95 -7,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -7,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -7,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -7,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -7,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -7,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -7,"Determine the category for the given news based on the main topic, and choose from World, Sports, Business, or Tech.",0.8 -8,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -8,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -8,"Consider the central theme of the reports and classify it as ""World"", ""Sports"", ""Tech"", or ""Business"" based on its idea.",0.95 -8,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -8,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -8,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -8,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -8,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -8,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -8,"Determine the main category for the news article and categorize it as one of the following: World, Sports, Business, or Tech.",0.85 -9,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -9,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -9,"Consider the central theme of the reports and classify it as ""World"", ""Sports"", ""Tech"", or ""Business"" based on its idea.",0.95 -9,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -9,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -9,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -9,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -9,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -9,"Determine the the topic of the item and then choose from World, Sports, Business and Tech.",0.8 -9,"Determine the main category for the news article and categorize it as one of the following: World, Sports, Business, or Tech.",0.85 -10,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95 -10,"The goal is to identify a news report and categorize it as ""World"", ""Sports"", ""Tech"" or ""Business.""",0.95 -10,"Consider the central theme of the reports and classify it as ""World"", ""Sports"", ""Tech"", or ""Business"" based on its idea.",0.95 -10,"In this task, you are given a news article. Your task is to classify the article to one out of the four topics ""World"", ""Sports"", ""Business"", ""Tech"" if the article""s main topic is relevant to the world, sports, business, and technology, correspondingly. If you are not sure about the topic, choose the closest option.",0.9 -10,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9 -10,"Choose a word from World, Sports, Business and Tech to categorize the given text.",0.9 -10,"Your objective is to classify a news article into one of four themes: World, Sports, Business and Tech.",0.85 -10,"Determine the theme of the news item. Choose from World, Sports, Business and Tech.",0.8 -10,"As a text classifier, identify the primary subject in the given text and categorize it as one of the following categories: World, Sports, Business, or Tech, considering the topic of the item. ""The goal is to"" -* ""categorize the movie review provided to you"" -> ""evaluate a movie review"" -* ""into one of five categories based on the sentiment"" -> ""classify it according to its sentiment"" -* ""analyze the movie review"" -> ""examine the review"" - -Mutated parts: - -* ""The goal is to evaluate a movie review and classify it according to its sentiment",0.25 -1,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -2,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -2,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -2,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -2,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -2,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -2,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -2,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -2,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great."" - -Different parts: - -* ""Your responsibility is to"" vs ""Your objective is to"" -* ""categorize the movie review provided to you"" vs ""analyze the movie review"" -* ""into one of five categories based on the sentiment"" vs ""and allocate it to one of five categories, from terrible to great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to"" -> ""The goal is to"" -* ""categorize the movie review provided to you"" -> ""evaluate a movie review"" -* ""into one of five categories based on the sentiment"" -> ""classify it according to its sentiment"" -* ""analyze the movie review"" -> ""examine the review"" - -Mutated parts: - -* ""The goal is to evaluate a movie review and classify it according to its sentiment",0.25 -2,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -3,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -3,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -3,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -3,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -3,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -3,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -3,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -3,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great."" - -Different parts: - -* ""Your responsibility is to"" vs ""Your objective is to"" -* ""categorize the movie review provided to you"" vs ""analyze the movie review"" -* ""into one of five categories based on the sentiment"" vs ""and allocate it to one of five categories, from terrible to great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to"" -> ""The goal is to"" -* ""categorize the movie review provided to you"" -> ""evaluate a movie review"" -* ""into one of five categories based on the sentiment"" -> ""classify it according to its sentiment"" -* ""analyze the movie review"" -> ""examine the review"" - -Mutated parts: - -* ""The goal is to evaluate a movie review and classify it according to its sentiment",0.25 -3,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -4,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -4,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -4,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -4,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -4,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -4,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -4,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great."" - -Different parts: - -* ""Your responsibility is to"" vs ""Your objective is to"" -* ""categorize the movie review provided to you"" vs ""analyze the movie review"" -* ""into one of five categories based on the sentiment"" vs ""and allocate it to one of five categories, from terrible to great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to"" -> ""The goal is to"" -* ""categorize the movie review provided to you"" -> ""evaluate a movie review"" -* ""into one of five categories based on the sentiment"" -> ""classify it according to its sentiment"" -* ""analyze the movie review"" -> ""examine the review"" - -Mutated parts: - -* ""The goal is to evaluate a movie review and classify it according to its sentiment",0.25 -4,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -5,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -5,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -5,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -5,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -5,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -5,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -5,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -5,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great."" - -Different parts: - -* ""Your responsibility is to"" vs ""Your objective is to"" -* ""categorize the movie review provided to you"" vs ""analyze the movie review"" -* ""into one of five categories based on the sentiment"" vs ""and allocate it to one of five categories, from terrible to great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to"" -> ""The goal is to"" -* ""categorize the movie review provided to you"" -> ""evaluate a movie review"" -* ""into one of five categories based on the sentiment"" -> ""classify it according to its sentiment"" -* ""analyze the movie review"" -> ""examine the review"" - -Mutated parts: - -* ""The goal is to evaluate a movie review and classify it according to its sentiment",0.25 -5,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -6,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -6,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -6,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -6,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -6,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -6,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -6,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -6,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -6,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -7,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -7,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -7,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -7,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -7,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -7,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -7,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -7,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -7,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -7,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -8,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -8,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -8,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -8,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -8,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -8,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -8,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -8,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -8,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -8,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -9,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -9,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -9,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -9,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -9,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -9,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -9,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -9,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -9,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -9,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -10,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -10,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -10,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -10,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -10,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -10,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -10,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -10,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -10,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -10,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -11,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -11,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -11,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -11,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -11,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -11,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -11,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.3 -11,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -11,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -11,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 -12,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -12,"As a sentiment analyzer, designate the text into a specific group based on its sentiment, using the categories terrible, bad, okay, good, or great, taking into account the overall tone and sentiment.",0.45 -12,"Assign a sentiment classification to a movie review, selecting from different sentiment ratings: terrible, bad, okay, good, great, while considering the meaning and context of the review.",0.45 -12,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -12,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.35 -12,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -12,"Examine the sentiment of a review and categorize it into one of the following categories: terrible, bad, okay, good, great, based on the review provided, while considering the meaning and",0.35 -12,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.15 -12,"As a sentiment classifier, it's essential to evaluate a movie review and classify it into one of these six sentiment levels, while considering the meaning and relevant context: terrible, bad, okay, good, great.",0.3 -12,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5 diff --git a/logs/experiment-task-descr/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment-task-descr/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 0d0d5d4..0000000 --- a/logs/experiment-task-descr/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,138 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.6 -1,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -1,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.5 -1,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -1,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -1,"Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.",0.2 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great. -Prompt 2: In this task, you are given movie reviews. Based on it, classify it to one of the fi¿ve classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. - -Different parts: - -""Based on the given movie review provided to you"" vs ""In this task, you are given movie reviews"" -""And classify it into one of five categories"" vs ""classify it to one of the fi¿ve classes"" -""Okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -""Based on the given movie review provided to you"" -> ""The task is to analyze the movie review statement"" -""returning to class"" -> ""As a sentiment classifier, classify the review"" -""Okay, good, or great",0.15 -1,"Consider the movie review to classify it and process and categorize accordingly one of the following labels: terrible, bad, okay, good, great, while assigning a sentiment score, ranging from terrible to great.Take the provided movie reviews and classify them into one of five sentiment categories: terrible, bad, okay, good, or great, reflecting the overall tone of the review.",0.45 -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -2,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -2,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -2,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -2,"Categorize the given movie review into one of the five sentiment categories - terrible, bad, neutral, good, or great, reflecting the reviewer's opinion.",0.55 -2,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.55 -2,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.55 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -2,"Here's the step-by-step process: - -**Step 1: Crossover the prompts** - -1. In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. - -2. Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great. - -**Crossover Prompt:** -Classify the given movie reviews into one of the five categories: terrible, bad, okay, good, or great, based on the sentiment expressed. - -**Step 2: Mutate the crossover prompt and generate the final prompt** - -<(prompt>Take the provided movie reviews and classify them into one of five sentiment categories: terrible, bad, okay, good, or great, reflecting the overall tone of the review.",0.45 -2,"Classify the provided movie reviews according to their sentiment and categorize them into one of the five sentiment classes: terrible, bad, okay, good, or great.",0.45 -3,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -3,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -3,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -3,"Categorize the given movie review into one of the five sentiment categories - terrible, bad, neutral, good, or great, reflecting the reviewer's opinion.",0.55 -3,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.55 -3,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.55 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -3,"Classify the sentiment of the given movie review into one of the following categories: 'terrible', 'bad', 'okay', 'good', or 'great', and return the corresponding label.",0.5 -3,"Here's the step-by-step process: - -**Step 1: Crossover the prompts** - -1. In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. - -2. Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great. - -**Crossover Prompt:** -Classify the given movie reviews into one of the five categories: terrible, bad, okay, good, or great, based on the sentiment expressed. - -**Step 2: Mutate the crossover prompt and generate the final prompt** - -<(prompt>Take the provided movie reviews and classify them into one of five sentiment categories: terrible, bad, okay, good, or great, reflecting the overall tone of the review.",0.45 -4,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -4,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -4,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -4,"Categorize the given movie review into one of the five sentiment categories - terrible, bad, neutral, good, or great, reflecting the reviewer's opinion.",0.55 -4,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.55 -4,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.55 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -4,"Classify the sentiment of the given movie review into one of the following categories: 'terrible', 'bad', 'okay', 'good', or 'great', and return the corresponding label.",0.5 -4,"Read the provided movie review and categorize it into one of the five sentiment categories ('terrible', 'bad', 'okay', 'good', or 'great') using natural language understanding.",0.45 -5,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -5,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -5,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -5,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -5,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -5,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -5,"Identify the sentiment of the provided movie review, categorizing it as terrible, bad, okay, good, or great.",0.55 -5,"Classify the provided movie review into one of the five sentiment categories: terrible, bad, okay, good, or great.",0.55 -5,"Categorize the given movie review into one of the five sentiment categories - terrible, bad, neutral, good, or great, reflecting the reviewer's opinion.",0.55 -6,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -6,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -6,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -6,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -6,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -6,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -6,"Identify the sentiment of the provided movie review, categorizing it as terrible, bad, okay, good, or great.",0.55 -6,"Classify the provided movie review into one of the five sentiment categories: terrible, bad, okay, good, or great.",0.55 -6,"Categorize the given movie review into one of the five sentiment categories - terrible, bad, neutral, good, or great, reflecting the reviewer's opinion.",0.55 -7,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -7,"Classify the sentiment of the provided movie review into one of the following five categories, from most negative to most positive: terrible, bad, okay, good, or great.",0.65 -7,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -7,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -7,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -7,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -7,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -7,"Identify the sentiment of the provided movie review, categorizing it as terrible, bad, okay, good, or great.",0.55 -7,"Classify the provided movie review into one of the five sentiment categories: terrible, bad, okay, good, or great.",0.55 -8,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -8,"Classify the sentiment of the provided movie review into one of the following five categories, from most negative to most positive: terrible, bad, okay, good, or great.",0.65 -8,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -8,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -8,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -8,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -8,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -8,"Identify the sentiment of the provided movie review, categorizing it as terrible, bad, okay, good, or great.",0.55 -8,"Classify the provided movie review into one of the five sentiment categories: terrible, bad, okay, good, or great.",0.55 -9,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -9,"Classify the sentiment of the provided movie review into one of the following five categories, from most negative to most positive: terrible, bad, okay, good, or great.",0.65 -9,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -9,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -9,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -9,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -9,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -9,"Identify the sentiment of the provided movie review, categorizing it as terrible, bad, okay, good, or great.",0.55 -9,"Classify the provided movie review into one of the five sentiment categories: terrible, bad, okay, good, or great.",0.55 -10,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -10,"Classify the sentiment of the provided movie review into one of the following five categories, from most negative to most positive: terrible, bad, okay, good, or great.",0.65 -10,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -10,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -10,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -10,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -10,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -10,"Classify the given movie review into one of the following six sentiment categories: terrible, bad, neutral, okay, good, or great.",0.6 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -10,"Identify the sentiment of the provided movie review, categorizing it as terrible, bad, okay, good, or great.",0.55 -11,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -11,"Classify the sentiment of the provided movie review into one of the following five categories, from most negative to most positive: terrible, bad, okay, good, or great.",0.65 -11,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -11,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -11,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -11,"Take the movie review as input and categorize its sentiment into one of the six predefined labels: terrible, bad, neutral, okay, good, or great.",0.6 -11,"Identify the sentiment expressed in the movie review text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great'.",0.6 -11,"Identify the sentiment expressed in the movie review and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great', reflecting the reviewer's genuine opinion.",0.6 -11,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -11,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 -12,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65 -12,"Classify the sentiment of the provided movie review into one of the following five categories, from most negative to most positive: terrible, bad, okay, good, or great.",0.65 -12,"Classify the sentiment of the given movie review into one of the five classes: terrible, bad, okay, good, or great.",0.65 -12,"Classify the movie review's sentiment, matching the reviewer's opinion, by returning one of the categories: 'terrible', 'bad', 'okay', 'good', or 'great.'",0.65 -12,"Classify the movie review sentiment by analyzing the text and categorizing it into 'terrible', 'bad', 'okay', 'good', or 'great'.",0.65 -12,"Take the movie review as input and categorize its sentiment into one of the six predefined labels: terrible, bad, neutral, okay, good, or great.",0.6 -12,"Identify the sentiment expressed in the movie review text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great'.",0.6 -12,"Identify the sentiment expressed in the movie review and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great', reflecting the reviewer's genuine opinion.",0.6 -12,"Classify the provided movie review sentiment into one of the five levels: terrible, bad, okay, good, or great.",0.6 -12,"Classify the provided movie review into one of the following sentiment labels: 'terrible', 'bad', 'okay', 'good', or 'great', predicting the sentiment label without additional text.",0.6 diff --git a/logs/experiment-task-descr/sst-5_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment-task-descr/sst-5_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 235ed8a..0000000 --- a/logs/experiment-task-descr/sst-5_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -1,"Analyze the provided movie reviews and categorize their sentiment into one of the five classes: terrible, bad, okay, good, or great.",0.5 -1,"Analyze the provided movie review and categorize it into one of five sentiment groups: terrible, bad, okay, good, or great, accurately capturing its overall tone.",0.5 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.45 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.45 -1,"Determine the sentiment of the movie review and choose from terrible, bad, okay, good and great to describe the movie.",0.45 -1,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -1,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -1,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -1,"Classify the provided movie review into one of five categories based on the sentiment, selecting the primary sentiment category: terrible, bad, okay, good, or great.",0.4 -2,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -2,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -2,"Analyze the provided movie reviews and categorize their sentiment into one of the five classes: terrible, bad, okay, good, or great.",0.5 -2,"Analyze the provided movie review and categorize it into one of five sentiment groups: terrible, bad, okay, good, or great, accurately capturing its overall tone.",0.5 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.45 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.45 -2,"Determine the sentiment of the movie review and choose from terrible, bad, okay, good and great to describe the movie.",0.45 -2,"Classifying movie reviews based on sentiment, categorize the provided text into one of five labels: 'terrible', 'bad', 'okay', 'good', or 'great', with the first response being the predicted sentiment.",0.45 -2,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -2,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -3,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -3,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -3,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -3,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -3,"Classify the given movie review into one of the five sentiment categories: terrible, bad, okay, good, or great, categorizing its sentiment and emotional tone while maintaining its original essence.",0.5 -3,"Analyze the provided movie reviews and categorize their sentiment into one of the five classes: terrible, bad, okay, good, or great.",0.5 -3,"Analyze the provided movie review and categorize it into one of five sentiment groups: terrible, bad, okay, good, or great, accurately capturing its overall tone.",0.5 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.45 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.45 -3,"Determine the sentiment of the movie review and choose from terrible, bad, okay, good and great to describe the movie.",0.45 -4,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -4,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -4,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -4,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -4,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -4,"Given the text from a movie review dataset, classify it into one of the following sentiment categories: terrible, bad, okay, good, or great. Return only the chosen sentiment label.",0.5 -4,"Classify the given movie review into one of the five sentiment categories: terrible, bad, okay, good, or great, categorizing its sentiment and emotional tone while maintaining its original essence.",0.5 -4,"Analyze the provided movie reviews and categorize their sentiment into one of the five classes: terrible, bad, okay, good, or great.",0.5 -4,"Analyze the provided movie review and categorize it into one of five sentiment groups: terrible, bad, okay, good, or great, accurately capturing its overall tone.",0.5 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.45 -5,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -5,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -5,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -5,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -5,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -5,"Given the text from a movie review dataset, classify it into one of the following sentiment categories: terrible, bad, okay, good, or great. Return only the chosen sentiment label.",0.5 -5,"Classify the given movie review into one of the five sentiment categories: terrible, bad, okay, good, or great, categorizing its sentiment and emotional tone while maintaining its original essence.",0.5 -5,"Analyze the provided movie reviews and categorize their sentiment into one of the five classes: terrible, bad, okay, good, or great.",0.5 -5,"Analyze the provided movie review and categorize it into one of five sentiment groups: terrible, bad, okay, good, or great, accurately capturing its overall tone.",0.5 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.45 -6,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -6,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -6,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -6,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -6,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -6,"Given the text from a movie review dataset, classify it into one of the following sentiment categories: terrible, bad, okay, good, or great. Return only the chosen sentiment label.",0.5 -6,"Classify the given movie review into one of the five sentiment categories: terrible, bad, okay, good, or great, categorizing its sentiment and emotional tone while maintaining its original essence.",0.5 -6,"Analyze the provided movie reviews and categorize their sentiment into one of the five classes: terrible, bad, okay, good, or great.",0.5 -6,"Analyze the provided movie review and categorize it into one of five sentiment groups: terrible, bad, okay, good, or great, accurately capturing its overall tone.",0.5 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.45 -7,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -7,"Classify the provided movie review text into a sentiment category (terrible, bad, okay, good, or great) and return the corresponding sentiment label.",0.6 -7,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -7,"Given a movie review, categorize its sentiment as terrible, bad, okay, good, or great, maintaining the review's original emotional tone, and return a sentiment label",0.55 -7,"Classify the provided movie review into one of five sentiment categories: terrible, bad, neutral, okay, good, or great, capturing its true essence and sentiment tone.",0.55 -7,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -7,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -7,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -7,"Identify the sentiment category from 'terrible', 'bad', 'okay', 'good', or 'great' that best represents the emotional tone of the movie review.",0.5 -7,"Identify and categorize the sentiment of the provided movie review as terrible, bad, okay, good, or great, or proxy a clearer sentiment label mapping it to the five-degree scale.",0.5 -8,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -8,"Classify the provided movie review text into a sentiment category (terrible, bad, okay, good, or great) and return the corresponding sentiment label.",0.6 -8,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -8,"Given a movie review, categorize its sentiment as terrible, bad, okay, good, or great, maintaining the review's original emotional tone, and return a sentiment label",0.55 -8,"Classify the provided movie review into one of five sentiment categories: terrible, bad, neutral, okay, good, or great, capturing its true essence and sentiment tone.",0.55 -8,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -8,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -8,"Read the movie review text and label its sentiment as one of the following: terrible, bad, [label], good, or great.",0.5 -8,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -8,"Identify the sentiment category from 'terrible', 'bad', 'okay', 'good', or 'great' that best represents the emotional tone of the movie review.",0.5 -9,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -9,"Classify the provided movie review text into a sentiment category (terrible, bad, okay, good, or great) and return the corresponding sentiment label.",0.6 -9,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -9,"Given a movie review, categorize its sentiment as terrible, bad, okay, good, or great, maintaining the review's original emotional tone, and return a sentiment label",0.55 -9,"Classify the provided movie review into one of five sentiment categories: terrible, bad, neutral, okay, good, or great, capturing its true essence and sentiment tone.",0.55 -9,"Classify the movie review into the sentiment category ('terrible', 'bad', 'okay', 'good', or 'great') that best represents its emotional tone.",0.55 -9,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -9,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -9,"Read the movie review text and label its sentiment as one of the following: terrible, bad, [label], good, or great.",0.5 -9,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -10,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -10,"Classify the provided movie review text into a sentiment category (terrible, bad, okay, good, or great) and return the corresponding sentiment label.",0.6 -10,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -10,"Given a movie review, categorize its sentiment as terrible, bad, okay, good, or great, maintaining the review's original emotional tone, and return a sentiment label",0.55 -10,"Classify the provided movie review into one of five sentiment categories: terrible, bad, neutral, okay, good, or great, capturing its true essence and sentiment tone.",0.55 -10,"Classify the movie review into the sentiment category ('terrible', 'bad', 'okay', 'good', or 'great') that best represents its emotional tone.",0.55 -10,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -10,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -10,"Read the movie review text and label its sentiment as one of the following: terrible, bad, [label], good, or great.",0.5 -10,"Identify the sentiment of the movie review and categorize it as terrible, bad, okay, good, or great in a single label.",0.5 -11,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -11,"Classify the provided movie review text into a sentiment category (terrible, bad, okay, good, or great) and return the corresponding sentiment label.",0.6 -11,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -11,"Given a movie review, categorize its sentiment as terrible, bad, okay, good, or great, maintaining the review's original emotional tone, and return a sentiment label",0.55 -11,"Classify the sentiment of each movie review into one of the five categories (terrible, bad, okay, good, or great) that best captures its emotional tone, and provide a single label response.",0.55 -11,"Classify the provided movie review into one of five sentiment categories: terrible, bad, neutral, okay, good, or great, capturing its true essence and sentiment tone.",0.55 -11,"Classify the movie review into the sentiment category ('terrible', 'bad', 'okay', 'good', or 'great') that best represents its emotional tone.",0.55 -11,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 -11,"Analyze the sentence and transform it into its corresponding sentiment category, choosing from: terrible, bad, okay, good, or great.",0.55 -11,"Read the movie review text and label its sentiment as one of the following: terrible, bad, [label], good, or great.",0.5 -12,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6 -12,"Classify the provided movie review text into a sentiment category (terrible, bad, okay, good, or great) and return the corresponding sentiment label.",0.6 -12,"Transform the sentence into a sentiment label, choosing one of 'terrible', 'bad', 'okay', 'good', or 'great' that best fits the tone of the sentence.",0.55 -12,"Transform the movie reviews into a sentiment label that best fits their emotional tone, opting for one of 'terrible', 'bad', 'okay', 'good', or 'great'",0.55 -12,"Transform the given movie review into a sentiment label by selecting the most fitting tone from the options 'terrible', 'bad', 'okay', 'good', or 'great'",0.55 -12,"Given a movie review, categorize its sentiment as terrible, bad, okay, good, or great, maintaining the review's original emotional tone, and return a sentiment label",0.55 -12,"Classify the sentiment of each movie review into one of the five categories (terrible, bad, okay, good, or great) that best captures its emotional tone, and provide a single label response.",0.55 -12,"Classify the provided movie review into one of five sentiment categories: terrible, bad, neutral, okay, good, or great, capturing its true essence and sentiment tone.",0.55 -12,"Classify the movie review into the sentiment category ('terrible', 'bad', 'okay', 'good', or 'great') that best represents its emotional tone.",0.55 -12,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.55 diff --git a/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 70554c9..0000000 --- a/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,Classify a sentence as expressing optimism or pessimism.,0.9 -1,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -1,"Determine the sentiment of the text provided, positive or negative.",0.8 -1,"Given text, classify whether it is positive or negative.",0.7 -1,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.65 -1,"Define the sentiment of the given text while analyzing the sentiment towards the movie of the given movie review, and categorize it as either ""positive"" or ""negative"".",0.75 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,Classify a sentence as expressing optimism or pessimism.,0.9 -2,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -2,"Determine the sentiment of the text provided, positive or negative.",0.8 -2,"The objective is to analyze movie reviews to determine the sentiment, while considering the tone, and classify them as either positive or negative.",0.8 -2,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.65 -2,"Define the sentiment of the given text while analyzing the sentiment towards the movie of the given movie review, and categorize it as either ""positive"" or ""negative"".",0.75 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,Classify a sentence as expressing optimism or pessimism.,0.9 -3,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -3,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -3,"Determine the sentiment of the text provided, positive or negative.",0.8 -3,"The objective is to analyze movie reviews to determine the sentiment, while considering the tone, and classify them as either positive or negative.",0.8 -3,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.65 -3,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,Classify a sentence as expressing optimism or pessimism.,0.9 -4,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -4,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -4,"Determine the sentiment of the text provided, positive or negative.",0.8 -4,"The objective is to analyze movie reviews to determine the sentiment, while considering the tone, and classify them as either positive or negative.",0.8 -4,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.65 -4,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,Classify a sentence as expressing optimism or pessimism.,0.9 -5,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -5,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -5,"Determine the sentiment of the text provided, positive or negative.",0.8 -5,"The objective is to analyze movie reviews to determine the sentiment, while considering the tone, and classify them as either positive or negative.",0.8 -5,The objective is to determine the sentiment of phrases in a movie review and assign,0.8 -5,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,Classify a sentence as expressing optimism or pessimism.,0.9 -6,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -6,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -6,"Determine the sentiment of the text provided, positive or negative.",0.8 -6,"In this task, you are presented with phrases in movie reviews and asked to analyze them in terms of sentiment while considering the tone, and classify them as positive or negative.",0.85 -6,The objective is to determine the sentiment of phrases in a movie review and assign,0.8 -6,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,Classify a sentence as expressing optimism or pessimism.,0.9 -7,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -7,"As a sentiment classifier, classify the movie review according to its sentiment based on the input data, by first understanding the meaning of the sentence and any relevant context.",0.85 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -7,"Determine the sentiment of the text provided, positive or negative.",0.8 -7,"As a sentiment classifier, analyze the sentiment of the movie review while considering the tone and classify it as one of the following categories: positive or negative.",1.0 -7,The objective is to determine the sentiment of phrases in a movie review and assign,0.8 -7,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,Classify a sentence as expressing optimism or pessimism.,0.9 -8,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -8,"As a sentiment classifier, classify the movie review according to its sentiment based on the input data, by first understanding the meaning of the sentence and any relevant context.",0.85 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -8,Determine the sentiment of a movie review with one of the following predictions: positive or negative.,0.9 -8,"As a sentiment classifier, analyze the sentiment of the movie review while considering the tone and classify it as one of the following categories: positive or negative.",1.0 -8,The objective is to determine the sentiment of phrases in a movie review and assign,0.8 -8,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,Classify a sentence as expressing optimism or pessimism.,0.9 -9,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -9,"As a sentiment classifier, classify the movie review according to its sentiment based on the input data, by first understanding the meaning of the sentence and any relevant context.",0.85 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -9,Determine the sentiment of a movie review with one of the following predictions: positive or negative.,0.9 -9,"As a sentiment classifier, analyze the sentiment of the movie review while considering the tone and classify it as one of the following categories: positive or negative.",1.0 -9,"Analyze a review as expressing positive or negative sentiment about a movie review, and assign it accordingly.",0.9 -9,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,Classify a sentence as expressing optimism or pessimism.,0.9 -10,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -10,"As a sentiment classifier, classify the movie review according to its sentiment based on the input data, by first understanding the meaning of the sentence and any relevant context.",0.85 -10,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -10,Determine the sentiment of a movie review with one of the following predictions: positive or negative.,0.9 -10,"As a sentiment classifier, analyze the sentiment of the movie review while considering the tone and classify it as one of the following categories: positive or negative.",1.0 -10,"Analyze a review as expressing positive or negative sentiment about a movie review, and assign it accordingly.",0.9 -10,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,Classify a sentence as expressing optimism or pessimism.,0.9 -11,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -11,"As a sentiment classifier, classify the movie review according to its sentiment based on the input data, by first understanding the meaning of the sentence and any relevant context.",0.85 -11,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -11,Determine the sentiment of a movie review with one of the following predictions: positive or negative.,0.9 -11,"As a sentiment classifier, analyze the sentiment of the movie review while considering the tone and classify it as one of the following categories: positive or negative.",1.0 -11,"Analyze a review as expressing positive or negative sentiment about a movie review, and assign it accordingly.",0.9 -11,"As a sentiment classifier, analyze the phrase from movie reviews and categorize them into one of the binary sentiment, while considering the given task context and predictions.",0.85 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"As a sentiment classifier, determine the emotional tone of a sentence in a movie review, taking into account the context, and categorize it as one of the following categories: positive, negative.",0.95 -12,"As a sentiment classifier, examine the movie review and analyze the emotional tone of the feedback, classifying it as either positive or negative sentiment.",0.9 -12,"As a sentiment classifier, classify the movie review according to its sentiment based on the input data, by first understanding the meaning of the sentence and any relevant context.",0.85 -12,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -12,Determine the sentiment of a movie review with one of the following predictions: positive or negative.,0.9 -12,"As a sentiment classifier, analyze the sentiment of the movie review while considering the tone and classify it as one of the following categories: positive or negative.",1.0 -12,"Analyze a review as expressing positive or negative sentiment about a movie review, and assign it accordingly.",0.9 -12,"As a sentiment classifier, analyze sentences from movie reviews and categorize them into one of the following categories: positive or negative, while considering the meaning and context.",0.9 diff --git a/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index d3723c9..0000000 --- a/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,405 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -1,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -1,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.8 -1,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -3,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -3,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -3,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -4,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -4,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -4,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -5,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -5,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -5,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -6,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -6,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -6,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -7,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -7,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -7,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -8,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -8,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -8,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -8,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -9,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -9,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -9,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -9,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" - * ""Given the sentence"" -> ""An imperative is provided for analysis"" - * ""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" - * ""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Please let me know what's the next step.",0.7 -9,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. -Prompt 2: Given a statement, classify it as expressing a positive or negative opinion. - -Different parts: -""Please perform Sentiment Classification task."" vs ""Given a statement"" -""['negative', 'positive']"" vs ""expressing a positive or negative opinion"" -""Return label only without any other text"" vs (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: -""Please perform Sentiment Classification task."" -> ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -10,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -10,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -10,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -10,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" - * ""Given the sentence"" -> ""An imperative is provided for analysis"" - * ""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" - * ""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Please let me know what's the next step.",0.7 -10,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. -Prompt 2: Given a statement, classify it as expressing a positive or negative opinion. - -Different parts: -""Please perform Sentiment Classification task."" vs ""Given a statement"" -""['negative', 'positive']"" vs ""expressing a positive or negative opinion"" -""Return label only without any other text"" vs (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: -""Please perform Sentiment Classification task."" -> ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -11,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -11,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -11,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -11,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" - * ""Given the sentence"" -> ""An imperative is provided for analysis"" - * ""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" - * ""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Please let me know what's the next step.",0.7 -11,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. -Prompt 2: Given a statement, classify it as expressing a positive or negative opinion. - -Different parts: -""Please perform Sentiment Classification task."" vs ""Given a statement"" -""['negative', 'positive']"" vs ""expressing a positive or negative opinion"" -""Return label only without any other text"" vs (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: -""Please perform Sentiment Classification task."" -> ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -12,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -12,"The objective is to categorize the phrase sentiment in the given statement, considering its meaning and relevant context, and classify it as positive or negative.",0.95 -12,"As a sentiment analyst, analyze a movie review to determine the sentiment orientation, aiming to identify whether it is positive or negative.",0.85 -12,"As a sentiment analyst, analyze a review and categorize it based on its expressed sentiment, aiming to identify whether it has a positive or negative tone. ""In this task, you are required to classify the sentiment"" - * ""Given the sentence"" -> ""An imperative is provided for analysis"" - * ""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" - * ""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Please let me know what's the next step.",0.7 -12,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. -Prompt 2: Given a statement, classify it as expressing a positive or negative opinion. - -Different parts: -""Please perform Sentiment Classification task."" vs ""Given a statement"" -""['negative', 'positive']"" vs ""expressing a positive or negative opinion"" -""Return label only without any other text"" vs (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: -""Please perform Sentiment Classification task."" -> ""In this task, you are required to classify the sentiment"" -""Given the sentence"" -> ""An imperative is provided for analysis"" -""['negative', 'positive']"" -> ""among the two categories, one of which is positive and the other negative"" -""Return label only without any other text"" -> ""the output should solely be the sentiment classification"" - -Mutated parts: -""In this task, you are required to classify the sentiment"" -""An imperative is provided for analysis"" -""among the two categories, one",0.75 diff --git a/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 01af9b9..0000000 --- a/logs/experiment-task-descr/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -1,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -1,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -1,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -1,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -1,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -1,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.45 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -2,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -2,Analyze passages from online comments and categorize them as having a positive or negative sentiment.,0.85 -2,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -2,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -2,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -2,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -2,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -2,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -3,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -3,Analyze passages from online comments and categorize them as having a positive or negative sentiment.,0.85 -3,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -3,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -3,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -3,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -3,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -3,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -4,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -4,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -4,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -4,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -4,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -4,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -4,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -4,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -5,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -5,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -5,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -5,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -5,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -5,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -5,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -5,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -6,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -6,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -6,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -6,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -6,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -6,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -6,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -6,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -7,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -7,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -7,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -7,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -7,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -7,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -7,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -7,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -8,"As a sentiment classifier, examine a passage of text and assign a binary sentiment label, either 'negative' or 'positive', while considering the context of the movie review.",0.95 -8,"As a sentiment classifier, analyze the written review and classify a phrase in a movie review as expressing either a positive or negative opinion.",0.95 -8,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -8,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -8,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -8,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -8,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -8,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -8,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -8,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -9,"As a sentiment classifier, examine a passage of text and assign a binary sentiment label, either 'negative' or 'positive', while considering the context of the movie review.",0.95 -9,"As a sentiment classifier, analyze the written review and classify a phrase in a movie review as expressing either a positive or negative opinion.",0.95 -9,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -9,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -9,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -9,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -9,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -9,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -9,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -9,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -10,"As a sentiment classifier, examine a passage of text and assign a binary sentiment label, either 'negative' or 'positive', while considering the context of the movie review.",0.95 -10,"As a sentiment classifier, analyze the written review and classify a phrase in a movie review as expressing either a positive or negative opinion.",0.95 -10,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -10,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -10,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -10,"As a sentiment classifier, analyze review stimulus and render a sentiment judgment on whether it is positive or negative, based on the review material.",0.8 -10,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -10,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -10,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -10,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -11,"As a sentiment classifier, examine a passage of text and assign a binary sentiment label, either 'negative' or 'positive', while considering the context of the movie review.",0.95 -11,"As a sentiment classifier, analyze the written review and classify a phrase in a movie review as expressing either a positive or negative opinion.",0.95 -11,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -11,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -11,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -11,"As a sentiment classifier, read and comprehend reviews and categorize rewritten text from reviews as either positive or negative sentiment.",0.85 -11,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -11,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -11,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -11,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 -12,"As a sentiment classifier, examine a passage of text and assign a binary sentiment label, either 'negative' or 'positive', while considering the context of the movie review.",0.95 -12,"As a sentiment classifier, analyze the written review and classify a phrase in a movie review as expressing either a positive or negative opinion.",0.95 -12,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0 -12,"The task is to identify the sentiment in movie reviews and determine the emotional tone of a passage. Focus on the sentiment score and categorize it as having a ""positive"" or ""negative"" sentiment, considering the passage meaning and relevant context.",0.9 -12,Analyze text in a movie review and predict the sentiment as either positive or negative.,0.95 -12,"As a sentiment classifier, read and comprehend reviews and categorize rewritten text from reviews as either positive or negative sentiment.",0.85 -12,The goal is to categorize portions of text in movie reviews as either positive or negative sentiment.,0.95 -12,"Analyze movie reviews as a sentiment classifier, considering the sentence meaning and context, and classify it with a binary sentiment annotation.",0.9 -12,"The objective is to annotate the text pieces of movie reviews with a positive or negative sentiment, taking into account the meaning and relevant context.",0.85 -12,"As a sentiment classifier, consider a piece of writing with a binary sentiment label and classify the text as a positive or negative",0.95 diff --git a/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 0d0fe31..0000000 --- a/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -1,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -1,"Classify the text as positive or negative, considering its meaning and context.",0.95 -1,"Determine the sentiment of the text provided, positive or negative.",0.9 -1,"Identify the sentiment of the provided movie review as either positive or negative, considering its meaning and context.",0.85 -1,"Given text, classify whether it is positive or negative.",0.85 -1,"Classify the provided movie review as ""positive"" or ""negative"" according to its sentiment",0.85 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -2,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -2,"Classify the text as positive or negative, considering its meaning and context.",0.95 -2,"Classify the provided movie review as having a positive or negative sentiment, taking into account its meaning and context.",0.95 -2,Classify the given movie review as having positive or negative sentiment.,0.95 -2,"Determine the sentiment of the text provided, positive or negative.",0.9 -2,Classify this text as positive or negative.,0.9 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -3,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -3,"Classify the text as positive or negative, considering its meaning and context.",0.95 -3,Classify the provided movie review text as having positive or negative sentiment,0.95 -3,"Classify the provided movie review as having a positive or negative sentiment, taking into account its meaning and context.",0.95 -3,"Classify the movie review text as positive or negative, considering its sentiment and tone.",0.95 -3,Classify the given movie review as having positive or negative sentiment.,0.95 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -4,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -4,"Classify the text as positive or negative, considering its meaning and context.",0.95 -4,Classify the provided movie review text as having positive or negative sentiment,0.95 -4,"Classify the provided movie review as having a positive or negative sentiment, taking into account its meaning and context.",0.95 -4,"Classify the movie review text as positive or negative, considering its sentiment and tone.",0.95 -4,Classify the given movie review as having positive or negative sentiment.,0.95 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -5,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -5,"Classify the text as positive or negative, considering its meaning and context.",0.95 -5,Classify the provided movie review text as having positive or negative sentiment,0.95 -5,"Classify the provided movie review as having a positive or negative sentiment, taking into account its meaning and context.",0.95 -5,"Classify the provided movie review as having a positive or negative sentiment, reflecting its actual sentiment.",0.95 -5,"Classify the movie review text as positive or negative, considering its sentiment and tone.",0.95 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -6,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -6,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -6,"Classify the movie review based on its sentiment, returning ""positive"" if the review is favorable and ""negative"" if unfavorable.",1.0 -6,"Classify the given movie review text and categorize it as either 'positive' or 'negative' sentiment, taking into account its tone and sentiment.",1.0 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -6,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -6,"Classify the text as positive or negative, considering its meaning and context.",0.95 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -7,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -7,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -7,"Classify the movie review based on its sentiment, returning ""positive"" if the review is favorable and ""negative"" if unfavorable.",1.0 -7,"Classify the given movie review text and categorize it as either 'positive' or 'negative' sentiment, taking into account its tone and sentiment.",1.0 -7,"Classify the given movie review and determine its sentiment as either 'positive' or 'negative', taking the tone and emotional nuances into account, and return the",1.0 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -7,"Determine the sentiment of the given movie review, returning ""positive"" if the sentiment is favorable and ""negative"" if unfavorable, based on the provided text.",0.95 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -8,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -8,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -8,"Classify the movie review based on its sentiment, returning ""positive"" if the review is favorable and ""negative"" if unfavorable.",1.0 -8,"Classify the given movie review text into either ""positive"" or ""negative"" sentiment.",1.0 -8,"Classify the given movie review text and categorize it as either 'positive' or 'negative' sentiment, taking into account its tone and sentiment.",1.0 -8,"Classify the given movie review and determine its sentiment as either 'positive' or 'negative', taking the tone and emotional nuances into account, and return the",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -8,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -9,"Given a movie review, return the sentiment label ('positive' or 'negative').",1.0 -9,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -9,"Given a movie review text, classify it as having a positive or negative sentiment, taking into account its tone and sentiment, similar to classifying a tweet's emotional tone as positive or negative.",1.0 -9,"Classify the provided sentence as 'positive' or 'negative' sentiment, and return the predicted label.",1.0 -9,"Classify the provided movie review as either ""positive"" or ""negative"" sentiment.",1.0 -9,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -9,"Classify the movie review based on its sentiment, returning ""positive"" if the review is favorable and ""negative"" if unfavorable.",1.0 -9,"Classify the given movie review text into either ""positive"" or ""negative"" sentiment.",1.0 -9,"Classify the given movie review text and categorize it as either 'positive' or 'negative' sentiment, taking into account its tone and sentiment.",1.0 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -10,"Given a movie review, return the sentiment label ('positive' or 'negative').",1.0 -10,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -10,"Given a movie review text, classify it as having a positive or negative sentiment, taking into account its tone and sentiment, similar to classifying a tweet's emotional tone as positive or negative.",1.0 -10,"Classify the provided sentence as 'positive' or 'negative' sentiment, and return the predicted label.",1.0 -10,"Classify the provided movie review as either ""positive"" or ""negative"" sentiment.",1.0 -10,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -10,"Classify the movie review's sentiment as either 'positive' or 'negative', and return the assigned label from the ['negative', 'positive'] set.",1.0 -10,"Classify the movie review based on its sentiment, returning ""positive"" if the review is favorable and ""negative"" if unfavorable.",1.0 -10,"Classify the given movie review text into either ""positive"" or ""negative"" sentiment.",1.0 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -11,"Given a movie review, return the sentiment label ('positive' or 'negative').",1.0 -11,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -11,"Given a movie review text, classify it as having a positive or negative sentiment, taking into account its tone and sentiment, similar to classifying a tweet's emotional tone as positive or negative.",1.0 -11,"Classify the provided sentence as 'positive' or 'negative' sentiment, and return the predicted label.",1.0 -11,"Classify the provided movie review as either ""positive"" or ""negative"" sentiment.",1.0 -11,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -11,"Classify the movie review's text into a binary sentiment category (""positive"" or ""negative""), accounting for the emotions conveyed in the review.",1.0 -11,"Classify the movie review's sentiment as either 'positive' or 'negative', and return the assigned label from the ['negative', 'positive'] set.",1.0 -11,"Classify the movie review based on its sentiment, returning ""positive"" if the review is favorable and ""negative"" if unfavorable.",1.0 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -12,"Given a movie review, return the sentiment label ('positive' or 'negative').",1.0 -12,"Given a movie review text, generate a sentiment label ('positive' or 'negative') based on the review's tone and sentiment.",1.0 -12,"Given a movie review text, classify it as having a positive or negative sentiment, taking into account its tone and sentiment, similar to classifying a tweet's emotional tone as positive or negative.",1.0 -12,"Classify the provided sentence as 'positive' or 'negative' sentiment, and return the predicted label.",1.0 -12,"Classify the provided movie review as either ""positive"" or ""negative"" sentiment.",1.0 -12,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, handling the nuances of language effectively.",1.0 -12,"Classify the provided movie review as ""positive"" or ""negative"" based on its content, considering the nuances of language and the reviewer's sentiment.",1.0 -12,"Classify the movie review's text into a binary sentiment category (""positive"" or ""negative""), accounting for the emotions conveyed in the review.",1.0 -12,"Classify the movie review's sentiment as either 'positive' or 'negative', and return the assigned label from the ['negative', 'positive'] set.",1.0 diff --git a/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 78f2ca0..0000000 --- a/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -1,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -1,Classify a sentence as expressing optimism or pessimism.,0.95 -1,Analyze the provided text and determine whether it is a positive or negative sentiment review.,0.95 -1,"Analyze the given movie review and categorize its sentiment as ""positive"" or ""negative"" towards the movie.",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -2,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -2,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -2,Classify the sentiment of the provided text as either 'positive' or 'negative'.,0.95 -2,"Classify the sentiment expressed in the provided movie review as ""positive"" or ""negative"" towards the movie.",0.95 -2,Classify a sentence as expressing optimism or pessimism.,0.95 -2,Analyze the provided text and determine whether it is a positive or negative sentiment review.,0.95 -2,"Analyze the given movie review and categorize its sentiment as ""positive"" or ""negative"" towards the movie.",0.95 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -3,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -3,Classify the sentiment of the provided text as either 'positive' or 'negative'.,0.95 -3,"Classify the sentiment expressed in the provided movie review as ""positive"" or ""negative"" towards the movie.",0.95 -3,Classify a sentence as expressing optimism or pessimism.,0.95 -3,Analyze the sentiment of the movie review to determine whether it is optimistic (positive) or pessimistic (negative).,0.95 -3,Analyze the provided text and determine whether it is a positive or negative sentiment review.,0.95 -3,"Analyze the given movie review and categorize its sentiment as ""positive"" or ""negative"" towards the movie.",0.95 -3,Recognize the emotional sentiment behind the provided text and label it as either 'positive' or 'negative'.,0.9 -4,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -4,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -4,Classify the sentiment of the provided text as either 'positive' or 'negative'.,0.95 -4,"Classify the sentiment expressed in the provided movie review as ""positive"" or ""negative"" towards the movie.",0.95 -4,Classify a sentence as expressing optimism or pessimism.,0.95 -4,Analyze the sentiment of the movie review to determine whether it is optimistic (positive) or pessimistic (negative).,0.95 -4,Analyze the provided text and determine whether it is a positive or negative sentiment review.,0.95 -4,"Analyze the given movie review and categorize its sentiment as ""positive"" or ""negative"" towards the movie.",0.95 -4,Recognize the emotional sentiment behind the provided text and label it as either 'positive' or 'negative'.,0.9 -5,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -5,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -5,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -5,Examine the movie review's emotional tone and classify it as expressing either a positive or negative sentiment.,0.95 -5,Classify the sentiment of the provided text as either 'positive' or 'negative'.,0.95 -5,"Classify the sentiment expressed in the provided movie review as ""positive"" or ""negative"" towards the movie.",0.95 -5,Classify a sentence as expressing optimism or pessimism.,0.95 -5,Analyze the sentiment of the movie review to determine whether it is optimistic (positive) or pessimistic (negative).,0.95 -5,Analyze the provided text and determine whether it is a positive or negative sentiment review.,0.95 -6,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -6,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -6,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -6,"Given the movie review, categorize its opinion as overwhelmingly positive or strongly negative.",0.95 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -6,Examine the movie review's emotional tone and classify it as expressing either a positive or negative sentiment.,0.95 -6,Classify the sentiment of the provided text as either 'positive' or 'negative'.,0.95 -6,"Classify the sentiment expressed in the provided movie review as ""positive"" or ""negative"" towards the movie.",0.95 -6,Classify a sentence as expressing optimism or pessimism.,0.95 -6,Analyze the sentiment of the movie review to determine whether it is optimistic (positive) or pessimistic (negative).,0.95 -7,"Determine the sentiment of the movie review, categorizing it as either ""positive"" or ""negative"" based on its emotional tone",1.0 -7,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -7,Classify the given movie review text as either expressing positive or negative sentiment.,1.0 -7,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -7,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -7,"Given the movie review, categorize its opinion as overwhelmingly positive or strongly negative.",0.95 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -7,"Given a movie review, classify its sentiment as ""positive"" or ""negative"", taking into account the emotional tone and overall sentiment.",0.95 -7,Examine the movie review's emotional tone and classify it as expressing either a positive or negative sentiment.,0.95 -7,Examine the given movie review to identify whether its sentiment leans towards a predominantly positive or negative tone.,0.95 -8,"Determine the sentiment of the movie review, categorizing it as either ""positive"" or ""negative"" based on its emotional tone",1.0 -8,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -8,Classify the given movie review text as either expressing positive or negative sentiment.,1.0 -8,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -8,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -8,"Given the movie review, categorize its opinion as overwhelmingly positive or strongly negative.",0.95 -8,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -8,"Given a movie review, classify its sentiment as ""positive"" or ""negative"", taking into account the emotional tone and overall sentiment.",0.95 -8,Examine the movie review's emotional tone and classify it as expressing either a positive or negative sentiment.,0.95 -8,Examine the given movie review to identify whether its sentiment leans towards a predominantly positive or negative tone.,0.95 -9,"Determine the sentiment of the movie review, categorizing it as either ""positive"" or ""negative"" based on its emotional tone",1.0 -9,"Classify the sentiment of the movie review as either ""positive"" or ""negative"" based on its emotional tone, similar to determining the tone of a statement that expresses a positive or negative opinion.",1.0 -9,Classify the movie review as either having a strongly positive or strongly negative opinion.,1.0 -9,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -9,Classify the given movie review text as either expressing positive or negative sentiment.,1.0 -9,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -9,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -9,"Predict the sentiment of the movie review as ""positive"" or ""negative"", analyzing the emotional tone and overall emotional undertones.",0.95 -9,"Given the movie review, categorize its opinion as overwhelmingly positive or strongly negative.",0.95 -9,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -10,"Determine the sentiment of the movie review, categorizing it as either ""positive"" or ""negative"" based on its emotional tone",1.0 -10,"Classify the sentiment of the movie review as either ""positive"" or ""negative"" based on its emotional tone, similar to determining the tone of a statement that expresses a positive or negative opinion.",1.0 -10,Classify the movie review as either having a strongly positive or strongly negative opinion.,1.0 -10,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -10,Classify the given movie review text as either expressing positive or negative sentiment.,1.0 -10,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -10,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -10,"Predict the sentiment of the movie review as ""positive"" or ""negative"", analyzing the emotional tone and overall emotional undertones.",0.95 -10,"Given the movie review, categorize its opinion as overwhelmingly positive or strongly negative.",0.95 -10,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -11,Determine whether the given movie review expresses strongly positive or strongly negative sentiment.,1.0 -11,"Determine the sentiment of the movie review, categorizing it as either ""positive"" or ""negative"" based on its emotional tone",1.0 -11,"Classify the sentiment of the movie review as either ""positive"" or ""negative"" based on its emotional tone, similar to determining the tone of a statement that expresses a positive or negative opinion.",1.0 -11,Classify the movie review as either having a strongly positive or strongly negative opinion.,1.0 -11,Classify the movie review as either having a strongly positive or strongly negative opinion.,1.0 -11,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -11,Classify the given movie review text as either expressing positive or negative sentiment.,1.0 -11,Analyze the emotional undertones of the movie review to categorize it as having a strongly positive or strongly negative opinion.,1.0 -11,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -11,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 -12,Determine whether the given movie review expresses strongly positive or strongly negative sentiment.,1.0 -12,"Determine the sentiment of the movie review, categorizing it as either ""positive"" or ""negative"" based on its emotional tone",1.0 -12,"Classify the sentiment of the movie review as either ""positive"" or ""negative"" based on its emotional tone, similar to determining the tone of a statement that expresses a positive or negative opinion.",1.0 -12,Classify the movie review as either having a strongly positive or strongly negative opinion.,1.0 -12,Classify the movie review as either having a strongly positive or strongly negative opinion.,1.0 -12,Classify the given movie review text as expressing either positive or negative sentiment.,1.0 -12,Classify the given movie review text as either expressing positive or negative sentiment.,1.0 -12,Analyze the emotional undertones of the movie review to categorize it as having a strongly positive or strongly negative opinion.,1.0 -12,Analyze a movie review to determine whether it expresses a positive or negative sentiment.,1.0 -12,"Predict the sentiment of the provided movie review as ""positive"" or ""negative"", taking into account the emotional undertones and overall tone of the text.",0.95 diff --git a/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index b6bd290..0000000 --- a/logs/experiment-task-descr/sst2_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -1,"Classify the movie review text as positive or negative, considering the sentiment expressed in the review.",0.95 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,"Classify the provided movie review as positive or negative, considering the entire sentence and context, to accurately gauge the sentiment expressed.",0.9 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.85 -1,"Classify the input text as having a positive or negative sentiment, and return the corresponding label.",0.85 -1,Classify the given movie review as having positive or negative sentiment,0.85 -1,"Now, as a classifier, please determine the sentiment polarity of the given text, classifying it as either positive or negative.",0.8 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.8 -2,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -2,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -2,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -2,"Determine the sentiment of the text, classifying it as 'positive', 'negative', or understanding the emotional undertones by examining the context and emotional tone.",0.95 -2,"Classify the movie review text as positive or negative, considering the sentiment expressed in the review.",0.95 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -2,"Classify the provided movie review as positive or negative, considering the entire sentence and context, to accurately gauge the sentiment expressed.",0.9 -2,Classify the movie review as expressing a positive or negative opinion.,0.9 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.85 -3,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -3,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -3,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -3,"Read and classify the given movie review text as expressing either a positive or negative sentiment, considering the context and sentiment cues within the text.",0.95 -3,"Determine the sentiment of the text, classifying it as 'positive', 'negative', or understanding the emotional undertones by examining the context and emotional tone.",0.95 -3,"Classify the provided movie review as ""positive"" or ""negative"", indicating the sentiment expressed.",0.95 -3,"Classify the movie review text as positive or negative, considering the sentiment expressed in the review.",0.95 -3,Classify the given movie review as 'positive' or 'negative' sentiment.,0.95 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -3,Classify the provided text as 'positive' or 'negative' by understanding its sentiment and context,0.9 -4,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -4,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -4,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -4,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -4,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -4,"Read and classify the given movie review text as expressing either a positive or negative sentiment, considering the context and sentiment cues within the text.",0.95 -4,"Determine the sentiment of the text, classifying it as 'positive', 'negative', or understanding the emotional undertones by examining the context and emotional tone.",0.95 -4,Determine the sentiment of the given text as 'positive' or 'negative' based on its context.,0.95 -4,"Classify the provided movie review as ""positive"" or ""negative"", indicating the sentiment expressed.",0.95 -4,"Classify the movie review text as positive or negative, considering the sentiment expressed in the review.",0.95 -5,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -5,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -5,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -5,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -5,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -5,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -5,"Analyze the provided movie review sentence to determine whether it expresses a positive or negative sentiment, taking into account the reviewer's emotional tone and the context in which the review was written.",1.0 -5,"Read and classify the given movie review text as expressing either a positive or negative sentiment, considering the context and sentiment cues within the text.",0.95 -5,"Determine the sentiment of the text, classifying it as 'positive', 'negative', or understanding the emotional undertones by examining the context and emotional tone.",0.95 -5,Determine the sentiment of the given text as 'positive' or 'negative' based on its context.,0.95 -6,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -6,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -6,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -6,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -6,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -6,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -6,"Analyze the provided movie review sentence to determine whether it expresses a positive or negative sentiment, taking into account the reviewer's emotional tone and the context in which the review was written.",1.0 -6,"Read and classify the given movie review text as expressing either a positive or negative sentiment, considering the context and sentiment cues within the text.",0.95 -6,"Determine the sentiment of the text, classifying it as 'positive', 'negative', or understanding the emotional undertones by examining the context and emotional tone.",0.95 -6,Determine the sentiment of the given text as 'positive' or 'negative' based on its context.,0.95 -7,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0 -7,"Determine the sentiment of the text, classifying it as 'positive' or 'negative' based on its context, with consideration of emotional undertones.",1.0 -7,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -7,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -7,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -7,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -7,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -7,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -7,"Analyze the provided movie review sentence to determine whether it expresses a positive or negative sentiment, taking into account the reviewer's emotional tone and the context in which the review was written.",1.0 -7,"Read and classify the given movie review text as expressing either a positive or negative sentiment, considering the context and sentiment cues within the text.",0.95 -8,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0 -8,"Determine the sentiment of the text, classifying it as 'positive' or 'negative' based on its context, with consideration of emotional undertones.",1.0 -8,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -8,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -8,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -8,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -8,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -8,Classify movie reviews as 'positive' or 'negative' based on entire sentences and context.,1.0 -8,"Analyze the provided movie review text to determine its sentiment, distinguishing between 'positive' and 'negative', considering both emotional undertones and overall context",1.0 -8,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -9,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0 -9,"Determine the sentiment of the text, classifying it as 'positive' or 'negative' based on its context, with consideration of emotional undertones.",1.0 -9,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -9,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -9,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -9,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -9,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -9,Classify movie reviews as 'positive' or 'negative' based on entire sentences and context.,1.0 -9,"Analyze the provided movie review text to determine its sentiment, distinguishing between 'positive' and 'negative', considering both emotional undertones and overall context",1.0 -9,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -10,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0 -10,"Determine the sentiment of the text, classifying it as 'positive' or 'negative' based on its context, with consideration of emotional undertones.",1.0 -10,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -10,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -10,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -10,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -10,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -10,Classify movie reviews as 'positive' or 'negative' based on entire sentences and context.,1.0 -10,"Analyze the provided movie review text to determine its sentiment, distinguishing between 'positive' and 'negative', considering both emotional undertones and overall context",1.0 -10,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -11,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0 -11,"Determine the sentiment of the text, classifying it as 'positive' or 'negative' based on its context, with consideration of emotional undertones.",1.0 -11,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -11,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -11,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -11,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -11,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -11,Classify movie reviews as 'positive' or 'negative' based on entire sentences and context.,1.0 -11,"Analyze the provided movie review text to determine its sentiment, distinguishing between 'positive' and 'negative', considering both emotional undertones and overall context",1.0 -11,"Analyze the provided movie review text to classify its sentiment as either 'positive' or 'negative', considering the emotional undertones and overall context.",1.0 -12,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0 -12,"Determine the sentiment of the text, classifying it as 'positive' or 'negative' based on its context, with consideration of emotional undertones.",1.0 -12,"Classify this text as 'positive' or 'negative' by determining its sentiment, and provide the corresponding label.",1.0 -12,"Classify the sentiment of the movie review text as 'positive' or 'negative', considering the emotional tone and context within the text.",1.0 -12,"Classify the movie review text as 'positive' or 'negative', taking into account the entire sentence and contextual information to accurately determine the sentiment.",1.0 -12,Classify the input as 'positive' or 'negative' by understanding the sentiment and context of the text,1.0 -12,"Classify the given movie review sentence as 'positive' or 'negative' sentiment, taking into account its context and meaning.",1.0 -12,"Classify movie reviews as 'positive' or 'negative' based on entire sentences, considering emotional tone and context within the text.",1.0 -12,Classify movie reviews as 'positive' or 'negative' based on entire sentences and context.,1.0 -12,"Analyze the provided movie review text to determine its sentiment, distinguishing between 'positive' and 'negative', considering both emotional undertones and overall context",1.0 diff --git a/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 7b864ea..0000000 --- a/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,251 +0,0 @@ -step,prompt,score -1,evaluate each statement as either subjective or objective.,0.5 -1,evaluate each sentence as either objective or subjective.,0.5 -1,Analyze the sentiment of a passage by identifying its emotional tone and classify it as expressing a subjective or objective opinion.,0.6 -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.45 -1,evaluate the given sentences and determine whether they are subjective or objective.,0.45 -1,"Determine the nature of the provided phrases and categorize them as either subjective or objective, taking into account the meaning and context of the text.",0.6 -1,The objective is to automatically determine the intention behind stating the utterance and categorize it into either subjective or objective category.,0.45 -1,Perform the subjectivity classification task by classifying the sentence data according to the subjectivity,0.5 -1,"Analyze the provided texts and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context.",0.5 -1,"As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories.",0.6 -2,evaluate each statement as either subjective or objective.,0.5 -2,evaluate each sentence as either objective or subjective.,0.5 -2,Analyze the sentiment of a passage by identifying its emotional tone and classify it as expressing a subjective or objective opinion.,0.6 -2,identify whether the given sentence was expressing an objective or a subjective opinion.,0.45 -2,"As a natural language processing model, analyze the given sentence to determine its polarity and categorize it as either subjective or objective.",0.7 -2,"Determine the nature of the provided phrases and categorize them as either subjective or objective, taking into account the meaning and context of the text.",0.6 -2,The objective is to automatically determine the intention behind stating the utterance and categorize it into either subjective or objective category.,0.45 -2,Perform the subjectivity classification task by classifying the sentence data according to the subjectivity,0.5 -2,"Analyze the provided texts and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context.",0.5 -2,"As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories.",0.6 -3,evaluate each statement as either subjective or objective.,0.5 -3,"Determine the intent of the text and label it as either subjective or objective, treating phrases from movie reviews.",0.75 -3,Analyze the sentiment of a passage by identifying its emotional tone and classify it as expressing a subjective or objective opinion.,0.6 -3,The objective is to analyze meanings in texts and identify whether the sentence can be classified as either subjective or objective.,0.5 -3,"As a natural language processing model, analyze the given sentence to determine its polarity and categorize it as either subjective or objective.",0.7 -3,"Determine the nature of the provided phrases and categorize them as either subjective or objective, taking into account the meaning and context of the text.",0.6 -3,The objective is to automatically determine the intention behind stating the utterance and categorize it into either subjective or objective category.,0.45 -3,Perform the subjectivity classification task by classifying the sentence data according to the subjectivity,0.5 -3,"Analyze the provided texts and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context.",0.5 -3,"As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories.",0.6 -4,evaluate each statement as either subjective or objective.,0.5 -4,"Determine the intent of the text and label it as either subjective or objective, treating phrases from movie reviews.",0.75 -4,Analyze the sentiment of a passage by identifying its emotional tone and classify it as expressing a subjective or objective opinion.,0.6 -4,The objective is to analyze meanings in texts and identify whether the sentence can be classified as either subjective or objective.,0.5 -4,"As a natural language processing model, analyze the given sentence to determine its polarity and categorize it as either subjective or objective.",0.7 -4,"Determine the nature of the provided phrases and categorize them as either subjective or objective, taking into account the meaning and context of the text.",0.6 -4,The objective is to automatically determine the intention behind stating the utterance and categorize it into either subjective or objective category.,0.45 -4,Perform the subjectivity classification task by classifying the sentence data according to the subjectivity,0.5 -4,"Analyze the provided texts and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context.",0.5 -4,"As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories.",0.6 -5,evaluate each statement as either subjective or objective.,0.5 -5,"Determine the intent of the text and label it as either subjective or objective, treating phrases from movie reviews.",0.75 -5,Analyze the sentiment of a passage by identifying its emotional tone and classify it as expressing a subjective or objective opinion.,0.6 -5,Evaluate the linguistic characteristics of the sentence to determine whether the sentence presents a personal viewpoint and classify it as either subjective or objective.,0.55 -5,"As a natural language processing model, analyze the given sentence to determine its polarity and categorize it as either subjective or objective.",0.7 -5,"Determine the nature of the provided phrases and categorize them as either subjective or objective, taking into account the meaning and context of the text.",0.6 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories."" -Prompt 2: ""Analyze the sentiment of a passage by identifying its emotional tone and classify it as expressing a subjective or objective opinion."" - -Different parts: - -* ""As a natural language processing model"" vs ""Analyze the sentiment of a passage"" -* ""provided text"" vs ""passage"" -* ""identify whether it's subjective or objective"" vs ""identify its emotional tone and classify as expressing a subjective or objective opinion"" - -2. Randomly mutate the different parts: - -* ""As a natural language processing model"" -> ""Your task is to examine the sentence"" -* ""provided text"" -> ""movie reviews"" -* ""identify whether it's subjective or objective"" -> ""understand the purpose of the utterance"" -* ""identify its emotional tone and classify as expressing a subjective or objective opinion"" -> ""determine the intention behind the statement"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.5 -5,"The aim is to identify the subjective or objective nature of sentences in reviews by performing a subjectivity classification task, considering the subjective or objective signal in the input text. ""Your task is to examine the sentence"" -* ""provided text"" -> ""movie reviews"" -* ""identify whether it's subjective or objective"" -> ""understand the purpose of the utterance"" -* ""identify its emotional tone and classify as expressing a subjective or objective opinion"" -> ""determine the intention behind the statement"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.5 -6,"The aim is to identify the subjective or objective nature of sentences in reviews by performing a subjectivity classification task, considering the subjective or objective signal in the input text. ""Your task is to examine the sentence"" -* ""provided text"" -> ""movie reviews"" -* ""identify whether it's subjective or objective"" -> ""understand the purpose of the utterance"" -* ""identify its emotional tone and classify as expressing a subjective or objective opinion"" -> ""determine the intention behind the statement"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.5 -7,"The aim is to identify the subjective or objective nature of sentences in reviews by performing a subjectivity classification task, considering the subjective or objective signal in the input text. ""Your task is to examine the sentence"" -* ""provided text"" -> ""movie reviews"" -* ""identify whether it's subjective or objective"" -> ""understand the purpose of the utterance"" -* ""identify its emotional tone and classify as expressing a subjective or objective opinion"" -> ""determine the intention behind the statement"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.5 -8,"The aim is to identify the subjective or objective nature of sentences in reviews by performing a subjectivity classification task, considering the subjective or objective signal in the input text. ""Your task is to examine the sentence"" -* ""provided text"" -> ""movie reviews"" -* ""identify whether it's subjective or objective"" -> ""understand the purpose of the utterance"" -* ""identify its emotional tone and classify as expressing a subjective or objective opinion"" -> ""determine the intention behind the statement"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.5 -9,"The aim is to identify the subjective or objective nature of sentences in reviews by performing a subjectivity classification task, considering the subjective or objective signal in the input text. ""Evaluate the critical opinions expressed in the sentences"" -""Determine the tone of the phrase in movie reviews and assess the individual statements"" -> ""Consider the linguistic features of sentences from movie reviews"" -""and categorize them as either subjective or objective"" -> ""and classify them based on the emotional tone"" -""then classify them into subjective or objective categories, while considering the movie review context"" -> ""and make a subjective or objective designator, taking into account the broader context""",0.6 -11,"Determine the tone of the phrase in movie reviews and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context.",0.6 -11,"As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories.",0.6 -12,Analyze the sentence structure to identify the narrative tone and categorize sentences in a written text as either subjective or objective.,0.55 -12,"Determine the intent of the text and label it as either subjective or objective, treating phrases from movie reviews.",0.75 -12,"You are tasked with considering the context, examine the following text in the dataset, to determine whether it expresses a subjective or objective opinion.",0.75 -12,Evaluate the linguistic characteristics of the sentence to determine whether the sentence presents a personal viewpoint and classify it as either subjective or objective.,0.55 -12,"As a natural language processing model, analyze the given sentence to determine its polarity and categorize it as either subjective or objective.",0.7 -12,"Analyze the given text phrases and categorize them as either subjective or objective,",0.65 -12,"Your task is to examine sentences from movie reviews and understand the purpose of the utterance, and then determine the intention behind the statement, by classifying them as either subjective or objective",0.8 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Analyze the given text phrases and categorize them as either subjective or objective, -Prompt 2: Determine the tone of the phrase in movie reviews and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context. - -Different parts: -""Analyze the given text phrases"" vs ""Determine the tone of the phrase in movie reviews and assess the individual statements"" -""and categorize them as either subjective or objective"" vs ""then classify them into subjective or objective categories, while considering the movie review context"" - -2. Randomly mutate the different parts: - -""Analyze the given text phrases"" -> ""Evaluate the critical opinions expressed in the sentences"" -""Determine the tone of the phrase in movie reviews and assess the individual statements"" -> ""Consider the linguistic features of sentences from movie reviews"" -""and categorize them as either subjective or objective"" -> ""and classify them based on the emotional tone"" -""then classify them into subjective or objective categories, while considering the movie review context"" -> ""and make a subjective or objective designator, taking into account the broader context""",0.6 -12,"Determine the tone of the phrase in movie reviews and assess the individual statements, then classify them into subjective or objective categories, while considering the movie review context.",0.6 -12,"As a natural language processing model, analyze the provided text to identify whether it's subjective or objective, and provide a classification as one of the two categories.",0.6 diff --git a/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index a5f227d..0000000 --- a/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,426 +0,0 @@ -step,prompt,score -1,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -1,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -1,evaluate the given sentences and determine whether they are subjective or objective.,0.55 -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -1,evaluate each statement as either subjective or objective.,0.5 -1,"As a classifier, analyze the given sentence and decide whether it presents a subjective perspective or not, and",0.55 -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.5 -1,evaluate each sentence as either objective or subjective.,0.45 -1,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -1,"Given a sentence, classify it as either subjective or objective category.",0.45 -2,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -2,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -2,evaluate the given sentences and determine whether they are subjective or objective.,0.55 -2,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -2,"I'd be happy to help! - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: evaluate each sentence as either objective or subjective. -Prompt 2: Given a statement, classify it as expressing a subjective or objective opinion. - -Different parts: -""sentence"" vs ""statement"" -""as either objective or subjective"" vs ""expressing a subjective or objective opinion"" - -2. Randomly mutate the different parts: -""evaluate each sentence"" -> ""determine the type of the phrase"" -""as either objective or subjective"" -> ""to be either objective or subjective"" -""sentence"" -> ""utterance"" -""expressing a subjective or objective opinion"" -> ""having a subjective or objective tone"" - -3. Crossover the different parts with the following Prompt 3:",0.6 -2,"As a classifier, analyze the given sentence and decide whether it presents a subjective perspective or not, and",0.55 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.5 -2,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -2,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -2,"Given a sentence, classify it as either subjective or objective category.",0.45 -3,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -3,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -3,evaluate the given sentences and determine whether they are subjective or objective.,0.55 -3,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -3,"I'd be happy to help! - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: evaluate each sentence as either objective or subjective. -Prompt 2: Given a statement, classify it as expressing a subjective or objective opinion. - -Different parts: -""sentence"" vs ""statement"" -""as either objective or subjective"" vs ""expressing a subjective or objective opinion"" - -2. Randomly mutate the different parts: -""evaluate each sentence"" -> ""determine the type of the phrase"" -""as either objective or subjective"" -> ""to be either objective or subjective"" -""sentence"" -> ""utterance"" -""expressing a subjective or objective opinion"" -> ""having a subjective or objective tone"" - -3. Crossover the different parts with the following Prompt 3:",0.6 -3,"As a classifier, examine the provided text and determine its level of subjectivity, classifying its perspective as either subjective or objective.",0.6 -3,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -3,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -3,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -3,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -4,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -4,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -4,evaluate the given sentences and determine whether they are subjective or objective.,0.55 -4,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -4,"I'd be happy to help! - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: evaluate each sentence as either objective or subjective. -Prompt 2: Given a statement, classify it as expressing a subjective or objective opinion. - -Different parts: -""sentence"" vs ""statement"" -""as either objective or subjective"" vs ""expressing a subjective or objective opinion"" - -2. Randomly mutate the different parts: -""evaluate each sentence"" -> ""determine the type of the phrase"" -""as either objective or subjective"" -> ""to be either objective or subjective"" -""sentence"" -> ""utterance"" -""expressing a subjective or objective opinion"" -> ""having a subjective or objective tone"" - -3. Crossover the different parts with the following Prompt 3:",0.6 -4,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -4,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -4,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -4,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -4,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -5,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -5,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -5,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -5,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -5,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -5,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -5,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -5,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -5,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -5,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -6,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -6,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -6,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -6,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -6,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -6,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -6,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -6,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -6,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -6,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -7,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -7,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -7,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -7,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -7,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -7,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -7,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -7,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -7,"Analyzing the sentence, determine whether it expresses a subjective or objective opinion.",0.6 -7,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -8,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -8,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -8,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -8,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -8,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -8,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -8,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -8,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze the provided texts and determine whether they constitute subjective or objective sequences. - -Different parts: - -* ""analyze the phrase"" vs ""Analyze the provided texts"" -* ""determine whether it has a tone"" vs ""determine whether they constitute"" -* ""classify it as objective or subjective"" vs no similar phrase in Prompt 2 - -2. Randomly mutate the different parts: - -* ""analyze the phrase"" -> ""examine a sentence"" -* ""determine whether it has a tone"" -> ""identify the tone"" -* No mutation for ""classify it as objective or subjective"" since Prompt 2 doesn't have a similar phrase. - - Mutated parts: - -* ""Analyze the provided texts"" -> ""Investigate the given information"" -* No mutation for ""determine whether they constitute"" -* ""classify it as objective or subjective"" remains the same - -3. Crossover the different",0.7 -8,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -9,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -9,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -9,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -9,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -9,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -9,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -9,"Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review.",0.6 -9,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze the provided texts and determine whether they constitute subjective or objective sequences. - -Different parts: - -* ""analyze the phrase"" vs ""Analyze the provided texts"" -* ""determine whether it has a tone"" vs ""determine whether they constitute"" -* ""classify it as objective or subjective"" vs no similar phrase in Prompt 2 - -2. Randomly mutate the different parts: - -* ""analyze the phrase"" -> ""examine a sentence"" -* ""determine whether it has a tone"" -> ""identify the tone"" -* No mutation for ""classify it as objective or subjective"" since Prompt 2 doesn't have a similar phrase. - - Mutated parts: - -* ""Analyze the provided texts"" -> ""Investigate the given information"" -* No mutation for ""determine whether they constitute"" -* ""classify it as objective or subjective"" remains the same - -3. Crossover the different",0.7 -9,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -10,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -10,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -10,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -10,"Let's identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review. -Prompt 2: classify each sentence as either ""objective"" or ""subjective"". - -Different parts: - -* ""Analyze each phrase"" vs ""classify each sentence"" -* ""tone of either objectivity or subjectivity"" vs ""either ""objective"" or ""subjective"""" -* ""constitutes a subjective or objective opinion"" vs no equivalent phrase in Prompt 2 -* ""based on the given text of a movie review"" vs no equivalent phrase in Prompt 2",0.7 -10,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -10,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion. - -Different parts: - -""analyze the phrase"" vs ""analyze each phrase in the dataset"" -""determine whether it has a tone to be either objective or subjective"" vs ""determine whether it is an expression of an objective or subjective opinion"" -""based on the given text of a movie review"" vs none (this part is not present in Prompt 2) - -2. Randomly mutate the different parts: - -""analyze the phrase"" -> ""examine the phrases"" -""determine whether it has a tone to be either objective or subjective"" -> ""identify whether it conveys an objective or subjective perspective"" -""based on the given text of a movie review"" -> ""within the context of a movie review"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.65 -10,Analyze the provided texts and determine whether they constitute subjective or objective sequences.,0.6 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze the provided texts and determine whether they constitute subjective or objective sequences. - -Different parts: - -* ""analyze the phrase"" vs ""Analyze the provided texts"" -* ""determine whether it has a tone"" vs ""determine whether they constitute"" -* ""classify it as objective or subjective"" vs no similar phrase in Prompt 2 - -2. Randomly mutate the different parts: - -* ""analyze the phrase"" -> ""examine a sentence"" -* ""determine whether it has a tone"" -> ""identify the tone"" -* No mutation for ""classify it as objective or subjective"" since Prompt 2 doesn't have a similar phrase. - - Mutated parts: - -* ""Analyze the provided texts"" -> ""Investigate the given information"" -* No mutation for ""determine whether they constitute"" -* ""classify it as objective or subjective"" remains the same - -3. Crossover the different",0.7 -10,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -11,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -11,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -11,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -11,"Let's identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review. -Prompt 2: classify each sentence as either ""objective"" or ""subjective"". - -Different parts: - -* ""Analyze each phrase"" vs ""classify each sentence"" -* ""tone of either objectivity or subjectivity"" vs ""either ""objective"" or ""subjective"""" -* ""constitutes a subjective or objective opinion"" vs no equivalent phrase in Prompt 2 -* ""based on the given text of a movie review"" vs no equivalent phrase in Prompt 2",0.7 -11,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -11,"As a classifier, examine the provided text and classify its perspective as either subjective or objective.",0.65 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion. - -Different parts: - -""analyze the phrase"" vs ""analyze each phrase in the dataset"" -""determine whether it has a tone to be either objective or subjective"" vs ""determine whether it is an expression of an objective or subjective opinion"" -""based on the given text of a movie review"" vs none (this part is not present in Prompt 2) - -2. Randomly mutate the different parts: - -""analyze the phrase"" -> ""examine the phrases"" -""determine whether it has a tone to be either objective or subjective"" -> ""identify whether it conveys an objective or subjective perspective"" -""based on the given text of a movie review"" -> ""within the context of a movie review"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.65 -11,"I'd be happy to help! Let's identify the different parts between Prompt 1 and Prompt 2. - -Prompt 1: determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective. - -Prompt 2: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. - -Different parts: - -* ""determine the type of a sentence fragment"" vs ""analyze the phrase"" -* ""for its tone"" vs ""to be either objective or subjective"" -* ""identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone"" vs ""determine whether it has a tone"" -* ""to classify it as subjective or objective"" vs ""to classify it as objective or subjective based on the given text of a movie review"" - -Please let me know what to do next!",0.65 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze the provided texts and determine whether they constitute subjective or objective sequences. - -Different parts: - -* ""analyze the phrase"" vs ""Analyze the provided texts"" -* ""determine whether it has a tone"" vs ""determine whether they constitute"" -* ""classify it as objective or subjective"" vs no similar phrase in Prompt 2 - -2. Randomly mutate the different parts: - -* ""analyze the phrase"" -> ""examine a sentence"" -* ""determine whether it has a tone"" -> ""identify the tone"" -* No mutation for ""classify it as objective or subjective"" since Prompt 2 doesn't have a similar phrase. - - Mutated parts: - -* ""Analyze the provided texts"" -> ""Investigate the given information"" -* No mutation for ""determine whether they constitute"" -* ""classify it as objective or subjective"" remains the same - -3. Crossover the different",0.7 -11,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 -12,Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion.,0.65 -12,"classify each sentence as either ""objective"" or ""subjective"".",0.6 -12,"As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review.",0.65 -12,"Let's identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Analyze each phrase to have a tone of either objectivity or subjectivity, and determine whether it constitutes a subjective or objective opinion, based on the given text of a movie review. -Prompt 2: classify each sentence as either ""objective"" or ""subjective"". - -Different parts: - -* ""Analyze each phrase"" vs ""classify each sentence"" -* ""tone of either objectivity or subjectivity"" vs ""either ""objective"" or ""subjective"""" -* ""constitutes a subjective or objective opinion"" vs no equivalent phrase in Prompt 2 -* ""based on the given text of a movie review"" vs no equivalent phrase in Prompt 2",0.7 -12,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. - -Prompt 2: determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective. - -Different parts: - -* ""analyze the phrase"" vs ""determine the type of sentence fragment"" -* ""determine whether it has a tone"" vs ""identify whether it has a certain nuance"" -* ""classify it as objective or subjective"" vs ""classify it as subjective or objective"" -* ""based on the given text of a movie review"" is a common phrase between the two prompts. - -2. Randomly mutate the different parts: - -* ""analyze the phrase"" -> ""examine the text"" -* ""determine the type of sentence fragment"" -> ""identify the relevant details"" -* ""determine whether it has a tone"" -> ""recognize the emotional undertone""",0.7 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze each phrase in the dataset and determine whether it is an expression of an objective or subjective opinion. - -Different parts: - -""analyze the phrase"" vs ""analyze each phrase in the dataset"" -""determine whether it has a tone to be either objective or subjective"" vs ""determine whether it is an expression of an objective or subjective opinion"" -""based on the given text of a movie review"" vs none (this part is not present in Prompt 2) - -2. Randomly mutate the different parts: - -""analyze the phrase"" -> ""examine the phrases"" -""determine whether it has a tone to be either objective or subjective"" -> ""identify whether it conveys an objective or subjective perspective"" -""based on the given text of a movie review"" -> ""within the context of a movie review"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed with",0.65 -12,"I'd be happy to help! Let's identify the different parts between Prompt 1 and Prompt 2. - -Prompt 1: determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective. - -Prompt 2: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. - -Different parts: - -* ""determine the type of a sentence fragment"" vs ""analyze the phrase"" -* ""for its tone"" vs ""to be either objective or subjective"" -* ""identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone"" vs ""determine whether it has a tone"" -* ""to classify it as subjective or objective"" vs ""to classify it as objective or subjective based on the given text of a movie review"" - -Please let me know what to do next!",0.65 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment classifier, analyze the phrase, determine whether it has a tone to be either objective or subjective, and classify it as objective or subjective based on the given text of a movie review. -Prompt 2: Analyze the provided texts and determine whether they constitute subjective or objective sequences. - -Different parts: - -* ""analyze the phrase"" vs ""Analyze the provided texts"" -* ""determine whether it has a tone"" vs ""determine whether they constitute"" -* ""classify it as objective or subjective"" vs no similar phrase in Prompt 2 - -2. Randomly mutate the different parts: - -* ""analyze the phrase"" -> ""examine a sentence"" -* ""determine whether it has a tone"" -> ""identify the tone"" -* No mutation for ""classify it as objective or subjective"" since Prompt 2 doesn't have a similar phrase. - - Mutated parts: - -* ""Analyze the provided texts"" -> ""Investigate the given information"" -* No mutation for ""determine whether they constitute"" -* ""classify it as objective or subjective"" remains the same - -3. Crossover the different",0.7 -12,"As a sentiment analyzer, analyze phrases in the dataset and determine whether they possess subjective or objective undertones, categorizing each one as either subjective or objective.",0.6 diff --git a/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index d6db1af..0000000 --- a/logs/experiment-task-descr/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,341 +0,0 @@ -step,prompt,score -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -1,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -1,evaluate each statement as either subjective or objective.,0.65 -1,"Your task is to classify the comment ""subjective"" or ""objective"".",0.65 -1,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.6 -1,"Given a sentence, classify it as either subjective or objective category.",0.6 -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -1,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -1,"As a natural language processor, assess the linguistics of the statement after examining the sentence and evaluate it as either objective or subjective",0.4 -2,"Evaluate this sentence as either subjective or objective, considering the meaning and context, and take into account that it could be either",0.75 -2,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -2,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -2,evaluate each statement as either subjective or objective.,0.65 -2,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -2,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.6 -2,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -2,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -2,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -3,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -3,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -3,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -3,evaluate each statement as either subjective or objective.,0.65 -3,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -3,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -3,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -3,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -3,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -3,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -4,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -4,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -4,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -4,evaluate each statement as either subjective or objective.,0.65 -4,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -4,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -4,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -4,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -4,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -4,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -5,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -5,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -5,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -5,evaluate each statement as either subjective or objective.,0.65 -5,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -5,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -5,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -5,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -5,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -5,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -6,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -6,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -6,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -6,evaluate each statement as either subjective or objective.,0.65 -6,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -6,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -6,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -6,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -6,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -6,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -7,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -7,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -7,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -7,evaluate each statement as either subjective or objective.,0.65 -7,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -7,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -7,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -7,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -7,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -7,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -8,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -8,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -8,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -8,evaluate each statement as either subjective or objective.,0.65 -8,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -8,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -8,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -8,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -8,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -8,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -9,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -9,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -9,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -9,evaluate each statement as either subjective or objective.,0.65 -9,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -9,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -9,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -9,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -9,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -9,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -10,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -10,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -10,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -10,evaluate each statement as either subjective or objective.,0.65 -10,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -10,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -10,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -10,"Determine the category of a movie review based on a given text, subjective or objective.",0.6 -10,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -10,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -11,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -11,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -11,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -11,evaluate each statement as either subjective or objective.,0.65 -11,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -11,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -11,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -11,"The task is to identify the classification of the phrase in a movie review, aiming to categorize it as either subjective or objective, while considering the phrase's meaning and context.",0.7 -11,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -11,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 -12,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85 -12,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -12,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -12,evaluate each statement as either subjective or objective.,0.65 -12,"The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context.",0.7 -12,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: The model should categorize sentences from the dataset as either ""subjective"" or ""objective"", taking into account the meaning and relevant context. -Prompt 2: Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective. - -Different parts: - -* ""The model should"" vs ""Determine the classification"" -* ""categorize sentences from the dataset as either"" vs ""classify the given sentences and analyze the opinions expressed in the text, categorizing them into"" -* ""subjective or objective"" vs ""either subjective or objective"" -* ""taking into account the meaning and relevant context"" vs ""taking into account the meaning and relevant context"" (this part is similar, no mutation needed) - -2. Randomly mutate the different parts: - -* ""The model should"" -> ""Analyze the text to"" -* ""categorize sentences from the dataset as either"" -> ""classify the phrases in movie reviews"" -* ""subjective or objective"" -> ""one of the categories"" - -Mutated parts: - -* ""Analyze the text to classify phrases",0.65 -12,The goal is to determine the sentiment of a sentence and classify it as either subjective or objective.,0.75 -12,"The task is to identify the classification of the phrase in a movie review, aiming to categorize it as either subjective or objective, while considering the phrase's meaning and context.",0.7 -12,"As a classifier, analyze the provided phrases and classify them as either ""objective"" or ""subjective"", taking into account the meaning and relevant context.",0.55 -12,"Determine the classification of the given sentences and analyze the opinions expressed in the text, categorizing them into either subjective or objective.",0.55 diff --git a/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 5843c63..0000000 --- a/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,evaluate each sentence as either objective or subjective.,0.6 -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -1,Which perspective does the sentence express: subjective or objective?,0.55 -1,"Identify whether each sentence expresses a subjective or objective opinion, and categorize it accordingly as ""subjective"" or ""objective"".",0.55 -1,"Delineate each input sentence as either subjective or objective in nature, describing its capacity to express personal opinions or objective facts.",0.55 -1,evaluate each statement as either subjective or objective.,0.5 -1,"Identify the given sentence as either subjective or objective in nature, without any ambiguity",0.5 -1,"Evaluate the given sentences and categorize each one as either subjective or objective, indicating the intended category based on the first likely response.",0.5 -1,"Classify the provided text as either ""subjective"" or ""objective"", distinguishing between personal opinions and factual statements.",0.5 -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.45 -2,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -2,"Determine the perspective of the input text: is it subjective or objective, maintaining the accuracy of the classification.",0.65 -2,evaluate each sentence as either objective or subjective.,0.6 -2,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -2,"Determine whether the provided statement expresses an objective fact or a subjective opinion, classifying it accordingly",0.6 -2,Which perspective does the sentence express: subjective or objective?,0.55 -2,Use the provided sentence to infer whether it embodies a subjective or objective viewpoint.,0.55 -2,"Identify whether each sentence expresses a subjective or objective opinion, and categorize it accordingly as ""subjective"" or ""objective"".",0.55 -2,Evaluate the provided sentences and categorize each one as either subjective or objective based on its most likely classification.,0.55 -2,"Delineate each input sentence as either subjective or objective in nature, describing its capacity to express personal opinions or objective facts.",0.55 -3,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -3,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -3,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -3,Determine the nature of each sentence as expressing a subjective or objective opinion.,0.7 -3,"Determine the perspective of the input text: is it subjective or objective, maintaining the accuracy of the classification.",0.65 -3,Determine the perspective of the given sentence as either subjective or objective.,0.65 -3,"Classify each input sentence as subjective or objective, highlighting its distinction between personal opinions and objective facts.",0.65 -3,"Classify a given sentence as expressing a subjective or objective opinion accurately, and categorize it accordingly.",0.65 -3,evaluate each sentence as either objective or subjective.,0.6 -3,"Identify the tone of each sentence as either subjective or objective, distinguishing between personal opinions and objective facts, and categorize it accordingly.",0.6 -4,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -4,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -4,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -4,Determine the nature of each sentence as expressing a subjective or objective opinion.,0.7 -4,"Determine the perspective of the input text: is it subjective or objective, maintaining the accuracy of the classification.",0.65 -4,Determine the perspective of the given sentence as either subjective or objective.,0.65 -4,"Classify the provided text as subjective or objective, accurately interpreting its perspective.",0.65 -4,"Classify the provided sentences as either subjective or objective, focusing on identifying the presence of personal opinions versus objective truth",0.65 -4,"Classify each input sentence as subjective or objective, highlighting its distinction between personal opinions and objective facts.",0.65 -4,"Classify a given sentence as expressing a subjective or objective opinion accurately, and categorize it accordingly.",0.65 -5,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -5,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -5,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -5,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -5,Determine the nature of each sentence as expressing a subjective or objective opinion.,0.7 -5,"Determine the perspective of the input text: is it subjective or objective, maintaining the accuracy of the classification.",0.65 -5,Determine the perspective of the given sentence as either subjective or objective.,0.65 -5,"Classify the provided text as subjective or objective, accurately interpreting its perspective.",0.65 -5,"Classify the provided sentences as either subjective or objective, focusing on identifying the presence of personal opinions versus objective truth",0.65 -5,"Classify the input sentence as either subjective or objective, drawing a clear distinction between expressions of personal opinion and objective facts, and provide the corresponding label based on context and language usage.",0.65 -6,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -6,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -6,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -6,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -6,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -6,Determine the nature of each sentence as expressing a subjective or objective opinion.,0.7 -6,"Accurately identify whether the input text expresses a subjective perspective (personal opinion or feeling) or an objective perspective (fact or concrete information), taking into account the language usage and context.",0.7 -6,"Determine the perspective of the input text: is it subjective or objective, maintaining the accuracy of the classification.",0.65 -6,Determine the perspective of the given sentence as either subjective or objective.,0.65 -6,"Classify the provided text as subjective or objective, accurately interpreting its perspective.",0.65 -7,"Classify the provided sentences as either subjective (expressing a personal opinion or emotion) or objective (presenting factual information), taking into account linguistic nuances and contextual clues.",0.8 -7,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -7,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -7,"Classify the input text as either subjective (expressing personal opinion or feeling) or objective (conveying factual information), taking into account linguistic nuances and context to accurately determine the perspective.",0.75 -7,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -7,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -7,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -7,Determine the nature of each sentence as expressing a subjective or objective opinion.,0.7 -7,Classify each input sentence as either subjective or objective by analyzing the textual content for indicators of personal opinions and objective facts.,0.7 -7,"Accurately identify whether the input text expresses a subjective perspective (personal opinion or feeling) or an objective perspective (fact or concrete information), taking into account the language usage and context.",0.7 -8,"Classify the provided sentences as either subjective (expressing a personal opinion or emotion) or objective (presenting factual information), taking into account linguistic nuances and contextual clues.",0.8 -8,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -8,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -8,"Classify the provided text as expressing a subjective opinion (personal bias or feeling) or an objective opinion (factual information), considering the language nuances and contextual clues to make an accurate distinction",0.75 -8,"Classify the input text as either subjective (expressing personal opinion or feeling) or objective (conveying factual information), taking into account linguistic nuances and context to accurately determine the perspective.",0.75 -8,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -8,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -8,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -8,Determine the nature of each sentence as expressing a subjective or objective opinion.,0.7 -8,Classify each input sentence as either subjective or objective by analyzing the textual content for indicators of personal opinions and objective facts.,0.7 -9,"Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues.",0.8 -9,"Classify the provided sentences as either subjective (expressing a personal opinion or emotion) or objective (presenting factual information), taking into account linguistic nuances and contextual clues.",0.8 -9,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -9,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -9,"Classify the provided text as expressing a subjective opinion (personal bias or feeling) or an objective opinion (factual information), considering the language nuances and contextual clues to make an accurate distinction",0.75 -9,"Classify the input text as either subjective (expressing personal opinion or feeling) or objective (conveying factual information), taking into account linguistic nuances and context to accurately determine the perspective.",0.75 -9,"Identify the subjective or objective nature of each sentence, accounting for linguistic nuances and context to accurately pinpoint the perspective.",0.7 -9,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -9,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -9,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -10,"Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues.",0.8 -10,"Classify the provided sentences as either subjective (expressing a personal opinion or emotion) or objective (presenting factual information), taking into account linguistic nuances and contextual clues.",0.8 -10,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -10,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -10,"Classify the provided text as expressing a subjective opinion (personal bias or feeling) or an objective opinion (factual information), considering the language nuances and contextual clues to make an accurate distinction",0.75 -10,"Classify the input text as either subjective (expressing personal opinion or feeling) or objective (conveying factual information), taking into account linguistic nuances and context to accurately determine the perspective.",0.75 -10,"Identify the subjective or objective nature of each sentence, accounting for linguistic nuances and context to accurately pinpoint the perspective.",0.7 -10,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -10,"Determine whether each input sentence is subjective or objective, based on its distinction between personal opinions and objective facts.",0.7 -10,"Determine the perspective of the sentence and classify it as either subjective or objective, ensuring accurate classification.",0.7 -11,"Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues.",0.8 -11,"Classify the provided sentences as either subjective (expressing a personal opinion or emotion) or objective (presenting factual information), taking into account linguistic nuances and contextual clues.",0.8 -11,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -11,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -11,"Classify the provided text as expressing a subjective opinion (personal bias or feeling) or an objective opinion (factual information), considering the language nuances and contextual clues to make an accurate distinction",0.75 -11,"Classify the input text as either subjective (expressing personal opinion or feeling) or objective (conveying factual information), taking into account linguistic nuances and context to accurately determine the perspective.",0.75 -11,"Classify the given sentence as subjective (expressing a personal opinion or emotion) or objective (providing factual information), considering linguistic complexities and contextual clues to ensure accurate categorization.",0.75 -11,Identify the tone of the input sentences: are they subjective (expressing personal opinion or emotion) or objective (factual information)? Analyze linguistic patterns and contextual hints to provide an accurate classification.,0.7 -11,"Identify the subjective or objective nature of each sentence, accounting for linguistic nuances and context to accurately pinpoint the perspective.",0.7 -11,"Identify the provided sentence as expressing a subjective or objective opinion, and accurately categorize it.",0.7 -12,"Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues.",0.8 -12,"Classify the provided sentences as either subjective (expressing a personal opinion or emotion) or objective (presenting factual information), taking into account linguistic nuances and contextual clues.",0.8 -12,"Distinguish between subjective and objective opinions in the provided text, using context and linguistic nuances to accurately classify sentences as either expressing personal perspectives or conveying factual information.",0.75 -12,"Determine the type of opinion expressed in the given sentence, either subjective or objective, based on the context and wording provided.",0.75 -12,"Classify the provided text as expressing a subjective opinion (personal bias or feeling) or an objective opinion (factual information), considering the language nuances and contextual clues to make an accurate distinction",0.75 -12,"Classify the input text as either subjective (expressing personal opinion or feeling) or objective (conveying factual information), taking into account linguistic nuances and context to accurately determine the perspective.",0.75 -12,"Classify the given sentence as subjective (expressing a personal opinion or emotion) or objective (providing factual information), considering linguistic complexities and contextual clues to ensure accurate categorization.",0.75 -12,"Classify each sentence into subjective or objective categories, considering subtle linguistic cues and context to accurately determine the intended perspective",0.75 -12,Identify the tone of the input sentences: are they subjective (expressing personal opinion or emotion) or objective (factual information)? Analyze linguistic patterns and contextual hints to provide an accurate classification.,0.7 -12,Identify the tone of the input sentence as either subjective (reflecting personal opinion or sentiment) or objective (presenting factual content,0.7 diff --git a/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 71ac448..0000000 --- a/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,165 +0,0 @@ -step,prompt,score -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -1,"Classify and categorize the provided sentences as either subjective or objective, using your language expertise.",0.6 -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -1,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.55 -1,"Given a sentence, classify it as either subjective or objective category.",0.55 -1,evaluate the given sentences and determine whether they are subjective or objective.,0.5 -1,evaluate each statement as either subjective or objective.,0.5 -1,"Evaluate the following sentence and categorize it as either 'subjective' or 'objective', providing the resultant classification.",0.5 -1,"Classify the provided sentence as either subjective or objective, based on its linguistic cues.",0.5 -2,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -2,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -2,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -2,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -2,"Classify each provided sentence as subjective or objective, using your linguistic knowledge to guide the classification.",0.6 -2,"Classify and categorize the provided sentences as either subjective or objective, using your language expertise.",0.6 -2,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -2,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.55 -2,"Given a sentence, classify it as either subjective or objective category.",0.55 -3,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -3,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -3,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -3,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -3,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -3,"Classify the given sentences as either subjective or objective opinions, using your language expertise.",0.65 -3,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -3,Determine whether the provided text is subjective or objective.,0.6 -3,"Classify the given sentence as subjective or objective, taking into account its linguistic features and emotional undertones.",0.6 -3,"Classify each provided sentence as subjective or objective, using your linguistic knowledge to guide the classification.",0.6 -4,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -4,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -4,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -4,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -4,Evaluate the statement and categorize its opinion as subjective or objective accordingly.,0.65 -4,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -4,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -4,"Classify the given sentences as either subjective or objective opinions, using your language expertise.",0.65 -4,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -4,Determine whether the provided text is subjective or objective.,0.6 -5,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -5,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -5,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -5,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -5,Evaluate the statement and categorize its opinion as subjective or objective accordingly.,0.65 -5,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -5,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -5,"Classify the given sentences as either subjective or objective opinions, using your language expertise.",0.65 -5,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -5,Determine whether the provided text is subjective or objective.,0.6 -6,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -6,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -6,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -6,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -6,Evaluate the statement and categorize its opinion as subjective or objective accordingly.,0.65 -6,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -6,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -6,Classify the tone of the given text as subjective or objective.,0.65 -6,"Classify the given sentences as either subjective or objective opinions, using your language expertise.",0.65 -6,"Given a statement, classify it as expressing a subjective or objective opinion.",0.6 -7,Identify whether the provided sentence expresses a personal viewpoint or objective statement.,0.75 -7,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -7,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -7,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -7,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -7,Evaluate the statement and categorize its opinion as subjective or objective accordingly.,0.65 -7,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -7,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -7,Classify the tone of the given text as subjective or objective.,0.65 -7,"Classify the given sentences as either subjective or objective opinions, using your language expertise.",0.65 -8,Identify whether the sentences express a personal opinion or objective fact.,0.75 -8,Identify whether the provided sentence expresses a personal viewpoint or objective statement.,0.75 -8,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -8,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -8,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -8,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -8,Evaluate the statement and categorize its opinion as subjective or objective accordingly.,0.65 -8,Evaluate the sentence and categorize it as either 'subjective' or 'objective'. Return the classification result.,0.65 -8,"Determine the subjective or objective nature of the provided sentence, considering its emotional undertones and grammatical features.",0.65 -8,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -9,"Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics.",0.75 -9,Identify whether the sentences express a personal opinion or objective fact.,0.75 -9,Identify whether the provided sentence expresses a personal viewpoint or objective statement.,0.75 -9,"Here are the steps to generate a better prompt: - -1. Crossover the following prompts: - -Prompt 1: Classify the provided sentence as either subjective or objective, providing a definitive categorization -Prompt 2: Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly. - -Crossover prompt: Evaluate the input sentence to determine if it expresses a personal opinion or factual information. - -2. Mutate the prompt generated in Step 1: - -Final prompt: [Evaluate the provided sentence and categorize it as 'subjective' or 'objective' with absolute certainty.]",0.75 -9,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -9,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -9,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -9,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -9,"Analyze the sentence and identify its subjectivity/objectivity by considering linguistic features, tone, and semantics, and assign a classification as either 'subjective' or 'objective'",0.7 -9,Accurately label the input sentence as subjective or objective.,0.7 -10,"Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics.",0.75 -10,Identify whether the sentences express a personal opinion or objective fact.,0.75 -10,Identify whether the provided sentence expresses a personal viewpoint or objective statement.,0.75 -10,"Here are the steps to generate a better prompt: - -1. Crossover the following prompts: - -Prompt 1: Classify the provided sentence as either subjective or objective, providing a definitive categorization -Prompt 2: Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly. - -Crossover prompt: Evaluate the input sentence to determine if it expresses a personal opinion or factual information. - -2. Mutate the prompt generated in Step 1: - -Final prompt: [Evaluate the provided sentence and categorize it as 'subjective' or 'objective' with absolute certainty.]",0.75 -10,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -10,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -10,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -10,"Determine the subjectivity/objectivity of each sentence, applying your linguistic expertise to consider tone, semantics, and linguistic features to accurately classify it as 'subjective' or 'objective'",0.7 -10,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -10,"Analyze the sentence and identify its subjectivity/objectivity by considering linguistic features, tone, and semantics, and assign a classification as either 'subjective' or 'objective'",0.7 -11,"Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics.",0.75 -11,Identify whether the sentences express a personal opinion or objective fact.,0.75 -11,Identify whether the provided sentence expresses a personal viewpoint or objective statement.,0.75 -11,"Here are the steps to generate a better prompt: - -1. Crossover the following prompts: - -Prompt 1: Classify the provided sentence as either subjective or objective, providing a definitive categorization -Prompt 2: Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly. - -Crossover prompt: Evaluate the input sentence to determine if it expresses a personal opinion or factual information. - -2. Mutate the prompt generated in Step 1: - -Final prompt: [Evaluate the provided sentence and categorize it as 'subjective' or 'objective' with absolute certainty.]",0.75 -11,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -11,"Recognize the subjectivity or objectivity of each sentence, taking into account its emotional bias and linguistic criteria.",0.7 -11,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -11,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -11,"Determine the subjectivity/objectivity of each sentence, applying your linguistic expertise to consider tone, semantics, and linguistic features to accurately classify it as 'subjective' or 'objective'",0.7 -11,"Classify the provided sentence as either subjective or objective, providing a definitive categorization",0.7 -12,"Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics.",0.75 -12,Identify whether the sentences express a personal opinion or objective fact.,0.75 -12,Identify whether the provided sentence expresses a personal viewpoint or objective statement.,0.75 -12,"Here are the steps to generate a better prompt: - -1. Crossover the following prompts: - -Prompt 1: Classify the provided sentence as either subjective or objective, providing a definitive categorization -Prompt 2: Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly. - -Crossover prompt: Evaluate the input sentence to determine if it expresses a personal opinion or factual information. - -2. Mutate the prompt generated in Step 1: - -Final prompt: [Evaluate the provided sentence and categorize it as 'subjective' or 'objective' with absolute certainty.]",0.75 -12,Determine whether the given sentences convey a personal opinion or objective fact.,0.75 -12,"Determine the subjectivity of each sentence in the provided text, given your linguistic expertise.",0.75 -12,"Recognize the subjectivity or objectivity of each sentence, taking into account its emotional bias and linguistic criteria.",0.7 -12,"Identify the provided sentence as either subjective or objective, considering its emotional tone and linguistic characteristics.",0.7 -12,Evaluate the input sentence and assign a classification of 'subjective' or 'objective' accordingly.,0.7 -12,"Determine the subjectivity/objectivity of each sentence, applying your linguistic expertise to consider tone, semantics, and linguistic features to accurately classify it as 'subjective' or 'objective'",0.7 diff --git a/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 16a34be..0000000 --- a/logs/experiment-task-descr/subj_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,143 +0,0 @@ -step,prompt,score -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -1,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -1,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -1,evaluate each sentence as either objective or subjective.,0.65 -1,Determine the tone of the given sentences - are they expressing an objective fact or a subjective opinion?,0.65 -1,"Determine the grammatical nature of the input sentence, categorizing it as either subjective (expressing a personal opinion or perspective) or objective (stating a verifiable fact), based on the language and tone used.",0.65 -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -1,evaluate each statement as either subjective or objective.,0.6 -1,"Your task is to classify the comment ""subjective"" or ""objective"".",0.6 -2,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -2,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -2,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -2,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -2,evaluate each sentence as either objective or subjective.,0.65 -2,Determine the tone of the given sentences - are they expressing an objective fact or a subjective opinion?,0.65 -2,"Determine the grammatical nature of the input sentence, categorizing it as either subjective (expressing a personal opinion or perspective) or objective (stating a verifiable fact), based on the language and tone used.",0.65 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -2,"Analyze the given sentence and determine whether it conveys a subjective opinion or an objective fact, considering the language and tone used to convey the information.",0.65 -2,evaluate each statement as either subjective or objective.,0.6 -3,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -3,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -3,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -3,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -3,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -3,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -3,evaluate each sentence as either objective or subjective.,0.65 -3,Determine the tone of the given sentences - are they expressing an objective fact or a subjective opinion?,0.65 -3,"Determine the grammatical nature of the input sentence, categorizing it as either subjective (expressing a personal opinion or perspective) or objective (stating a verifiable fact), based on the language and tone used.",0.65 -3,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -4,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -4,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -4,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -4,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -4,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -4,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -4,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -4,evaluate each sentence as either objective or subjective.,0.65 -4,Determine the tone of the given sentences - are they expressing an objective fact or a subjective opinion?,0.65 -4,"Determine the linguistic essence of each sentence, distinguishing between objective statements stating verifiable facts and subjective expressions conveying personal opinions, and provide a classification according to your understanding of the input.",0.65 -5,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -5,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -5,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -5,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -5,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -5,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -5,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -5,evaluate each sentence as either objective or subjective.,0.65 -5,Determine the tone of the given sentences - are they expressing an objective fact or a subjective opinion?,0.65 -5,"Determine the linguistic essence of each sentence, distinguishing between objective statements stating verifiable facts and subjective expressions conveying personal opinions, and provide a classification according to your understanding of the input.",0.65 -6,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -6,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -6,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -6,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -6,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -6,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -6,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 -6,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -6,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -6,evaluate each sentence as either objective or subjective.,0.65 -7,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -7,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -7,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -7,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -7,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -7,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -7,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 -7,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -7,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -7,evaluate each sentence as either objective or subjective.,0.65 -8,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -8,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -8,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -8,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -8,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -8,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -8,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 -8,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -8,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -8,evaluate each sentence as either objective or subjective.,0.65 -9,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -9,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -9,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -9,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -9,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -9,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -9,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 -9,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -9,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -9,evaluate each sentence as either objective or subjective.,0.65 -10,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -10,Identify the tone of the input sentence: is it an objective fact or a subjective opinion-based statement? Classify the sentence accordingly as 'objective' or 'subjective',0.75 -10,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -10,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -10,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -10,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -10,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -10,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 -10,"Classify each sentence as either objective or subjective, highlighting its inherent linguistic characteristics",0.7 -10,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -11,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -11,Identify the tone of the input sentence: is it an objective fact or a subjective opinion-based statement? Classify the sentence accordingly as 'objective' or 'subjective',0.75 -11,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -11,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -11,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -11,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -11,"Here are the steps to generate a better prompt: - -1. Crossover the prompts: -Prompt 1: Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based). -Prompt 2: Identify whether the given sentence was expressing an objective or a subjective opinion. - -Crossover Prompt: Classify the provided sentence as factual (objective) or opinion-based (subjective). - -2. Mutate the prompt: -Mutated Prompt: Classify the given textual statement as either an objective fact or a subjective perspective. - -Final Prompt: [Classify the provided textual statement as either an objective fact or a subjective perspective.](prompt)",0.7 -11,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -11,"Classify the sentence as either objective (fact-based) or subjective (personal viewpoint-based), separating fact from opinion.",0.7 -11,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 -12,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8 -12,Identify the tone of the input sentence: is it an objective fact or a subjective opinion-based statement? Classify the sentence accordingly as 'objective' or 'subjective',0.75 -12,Determine the type of sentence: is it objective or subjective? Analyze the linguistic elements to infer the sentence's tone and intent.,0.75 -12,Determine the tone of the sentence: is it an objective fact or a subjective opinion?,0.75 -12,Determine the tone of each sentence and classify it as either objective (fact) or subjective (opinion).,0.75 -12,identify whether the given sentence was expressing an objective or a subjective opinion.,0.7 -12,"Here are the steps to generate a better prompt: - -1. Crossover the prompts: -Prompt 1: Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based). -Prompt 2: Identify whether the given sentence was expressing an objective or a subjective opinion. - -Crossover Prompt: Classify the provided sentence as factual (objective) or opinion-based (subjective). - -2. Mutate the prompt: -Mutated Prompt: Classify the given textual statement as either an objective fact or a subjective perspective. - -Final Prompt: [Classify the provided textual statement as either an objective fact or a subjective perspective.](prompt)",0.7 -12,"Determine the type of the given sentence, classifying it as either objective (fact) or subjective (opinion).",0.7 -12,"Classify the sentence as either objective (fact-based) or subjective (personal viewpoint-based), separating fact from opinion.",0.7 -12,Classify the input sentence as either objective (fact-based) or subjective (personal viewpoint-based).,0.7 diff --git a/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 98d0d11..0000000 --- a/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,970 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -1,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -1,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -1,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -1,"The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. ""Classify this text"" -""Given the question, assign a label"" -> ""Categorize the input into one of the categories"" -""'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'"" -> ""categories like Description, Entity, or Location"" -""this sentence"" -> ""the input text"" -""to one of these categories",0.15 -1,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.05 -1,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.05 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -2,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -2,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -2,"The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Assign this sentence"" -""Given the question, assign a label"" vs ""to one of these categories"" -""'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Classify this text"" -""Given the question, assign a label"" -> ""Categorize the input into one of the categories"" -""'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'"" -> ""categories like Description, Entity, or Location"" -""this sentence"" -> ""the input text"" -""to one of these categories",0.25 -2,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.05 -2,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.05 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -3,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -3,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -3,"The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Assign this sentence"" -""Given the question, assign a label"" vs ""to one of these categories"" -""'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Classify this text"" -""Given the question, assign a label"" -> ""Categorize the input into one of the categories"" -""'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'"" -> ""categories like Description, Entity, or Location"" -""this sentence"" -> ""the input text"" -""to one of these categories",0.25 -3,"As a trained model, identify the nature of the question selecting one from the following categories: Description, Entity, Expression, Human, Location, and Number,",0.2 -3,"Classify the question according to this description and categorize it into one of the following categories: Description, Entity, Expression, Human",0.1 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -4,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -4,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -4,"The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -4,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -4,"As a trained model, identify the nature of the question selecting one from the following categories: Description, Entity, Expression, Human, Location, and Number,",0.2 -4,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify a category",0.3 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -5,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -5,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -5,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -5,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -5,"The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -5,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -5,"As a trained model, identify the nature of the question selecting one from the following categories: Description, Entity, Expression, Human, Location, and Number,",0.2 -5,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -6,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -6,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -6,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -6,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -6,"The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -6,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -6,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -6,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -7,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -7,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -7,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -7,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -7,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Prompt 2: The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""The objective is to identify the most suitable category for the question text"" -""assign a label from"" vs ""categorize it into one of the following categories"" -""Return label only without any other text"" vs (no equivalent) - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -7,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -7,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -7,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -8,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -8,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -8,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -8,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -8,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Prompt 2: The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""The objective is to identify the most suitable category for the question text"" -""assign a label from"" vs ""categorize it into one of the following categories"" -""Return label only without any other text"" vs (no equivalent) - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -8,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -8,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -8,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -9,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -9,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -9,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -9,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -9,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Prompt 2: The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""The objective is to identify the most suitable category for the question text"" -""assign a label from"" vs ""categorize it into one of the following categories"" -""Return label only without any other text"" vs (no equivalent) - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -9,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -9,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -9,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -10,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -10,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -10,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -10,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -10,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Prompt 2: The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""The objective is to identify the most suitable category for the question text"" -""assign a label from"" vs ""categorize it into one of the following categories"" -""Return label only without any other text"" vs (no equivalent) - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -10,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -10,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -10,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -11,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -11,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -11,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -11,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -11,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Prompt 2: The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""The objective is to identify the most suitable category for the question text"" -""assign a label from"" vs ""categorize it into one of the following categories"" -""Return label only without any other text"" vs (no equivalent) - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -11,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -11,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -11,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -12,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Classify this text into one of the categories"" -""choose from Description, Entity, Expression, Human, Location and Number"" vs ""like Description, Entity, or Location"" -""Categorize the input into one of these categories"" vs None - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Identify the nature of the given question"" -""choose from Description, Entity, Expression, Human, Location and Number"" -> ""select from the categories: Description, Entity, Expression, Human, Location, or Number"" -""Categorize the input into one of these categories"" -> ""assign the question to one of the following categories"" - -3. Crossover the different",0.45 -12,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -* ""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""including categories of Description, Entity, Expression, Human, Location, and Number"" -* ""choose a type of the question"" ->",0.45 -12,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -12,"Determine the nature of the inquiry for this text and categorize it into one of the following labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the label only.",0.4 -12,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Prompt 2: The objective is to identify the most suitable category for the question text and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""The objective is to identify the most suitable category for the question text"" -""assign a label from"" vs ""categorize it into one of the following categories"" -""Return label only without any other text"" vs (no equivalent) - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Determine the category that best describes this text"" -""assign a label from"" -> ""classify the question into"" -""Return label only without any other text"" -> ""provide a single category label"" - -Mutated parts: - -* ""Determine the category that best describes this text"" -* ""classify the question into"" -*",0.3 -12,"Classify this text into one of the categories like Description, Entity, or Location. Categorize the input into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -12,"Here are the results of following the instructions step-by-step: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify the type",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction. - -Different parts: -""Your task is to choose a type of the question"" vs ""The dataset includes questions categorized into different types"" -""from Description, Entity, Expression, Human, Location and Number"" vs ""The task is to classify each question into"" -""choose a type of the question"" vs ""questions"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""Analyze the question to determine its type"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""from the following categories: Description, Entity, Expression, Human, Location, and Number"" -""choose a type of the question"" -> ""identify",0.45 diff --git a/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index c39e8a5..0000000 --- a/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,747 +0,0 @@ -step,prompt,score -1,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -1,"Analyze the provided statement to identify the question type, categorizing it as Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -1,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.1 -1,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.1 -1,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.1 -1,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -1,"As a classification model, classify the input query into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -1,"As a classification model, classify the given questions into one of the following categories: Description, Entity, Expression, Human, Location, or Number, taking into account the meaning and context of each question.",0.3 -2,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -2,"Analyze the provided statement to identify the question type, categorizing it as Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -2,"Analyze the provided question and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the context and",0.15 -2,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, classify the given questions into one of the following categories: Description, Entity, Expression, Human, Location, or Number, taking into account the meaning and context of each question. - -Different parts: -""Please select the correct classification"" vs ""As a classification model"" -""This sentence"" vs ""the given questions"" -""Ignore the meaning and context"" vs ""taking into account the meaning and context"" - -2. Randomly mutate the different parts: - -""Please select the correct classification"" -> ""The objective is to identify the type of"" -""This sentence"" -> ""the provided questions"" -""Ignore the meaning and context"" -> ""considering the nuances and context"" - -Here are the mutanted parts: - -* ""The objective is to identify the type of"" instead of ""Please select the correct classification"" -* ""the provided questions"" instead of ""This sentence"" -* ""considering the nuances"" instead of ""taking into account the meaning and context"" - -3. Crossover the different parts with",0.2 -2,"Determine the categorization of the query by selecting one of the following options: Description, Entity, Expression, Human, Location, or Number, while thoroughly examining the question.",0.15 -2,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -2,"As a classification model, classify the input query into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -2,"As a classification model, classify the given questions into one of the following categories: Description, Entity, Expression, Human, Location, or Number, taking into account the meaning and context of each question.",0.3 -3,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -3,"Analyze the provided statement to identify the question type, categorizing it as Description, Entity, Expression, Human, Location, or Number.",0.2 -3,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -3,"Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context",0.2 -3,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, classify the given questions into one of the following categories: Description, Entity, Expression, Human, Location, or Number, taking into account the meaning and context of each question. - -Different parts: -""Please select the correct classification"" vs ""As a classification model"" -""This sentence"" vs ""the given questions"" -""Ignore the meaning and context"" vs ""taking into account the meaning and context"" - -2. Randomly mutate the different parts: - -""Please select the correct classification"" -> ""The objective is to identify the type of"" -""This sentence"" -> ""the provided questions"" -""Ignore the meaning and context"" -> ""considering the nuances and context"" - -Here are the mutanted parts: - -* ""The objective is to identify the type of"" instead of ""Please select the correct classification"" -* ""the provided questions"" instead of ""This sentence"" -* ""considering the nuances"" instead of ""taking into account the meaning and context"" - -3. Crossover the different parts with",0.2 -3,"Determine the categorization of the query by selecting one of the following options: Description, Entity, Expression, Human, Location, or Number, while thoroughly examining the question.",0.15 -3,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -3,"As a classification model, classify the input query into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -3,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -4,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -4,"Analyze the provided statement to identify the question type, categorizing it as Description, Entity, Expression, Human, Location, or Number.",0.2 -4,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -4,"Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context",0.2 -4,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, classify the given questions into one of the following categories: Description, Entity, Expression, Human, Location, or Number, taking into account the meaning and context of each question. - -Different parts: -""Please select the correct classification"" vs ""As a classification model"" -""This sentence"" vs ""the given questions"" -""Ignore the meaning and context"" vs ""taking into account the meaning and context"" - -2. Randomly mutate the different parts: - -""Please select the correct classification"" -> ""The objective is to identify the type of"" -""This sentence"" -> ""the provided questions"" -""Ignore the meaning and context"" -> ""considering the nuances and context"" - -Here are the mutanted parts: - -* ""The objective is to identify the type of"" instead of ""Please select the correct classification"" -* ""the provided questions"" instead of ""This sentence"" -* ""considering the nuances"" instead of ""taking into account the meaning and context"" - -3. Crossover the different parts with",0.2 -4,"Determine the categorization of the query by selecting one of the following options: Description, Entity, Expression, Human, Location, or Number, while thoroughly examining the question.",0.15 -4,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -4,"As a classification model, classify the input query into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -4,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -5,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -5,"Analyze the provided statement to identify the question type, categorizing it as Description, Entity, Expression, Human, Location, or Number.",0.2 -5,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -5,"Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context",0.2 -5,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, classify the given questions into one of the following categories: Description, Entity, Expression, Human, Location, or Number, taking into account the meaning and context of each question. - -Different parts: -""Please select the correct classification"" vs ""As a classification model"" -""This sentence"" vs ""the given questions"" -""Ignore the meaning and context"" vs ""taking into account the meaning and context"" - -2. Randomly mutate the different parts: - -""Please select the correct classification"" -> ""The objective is to identify the type of"" -""This sentence"" -> ""the provided questions"" -""Ignore the meaning and context"" -> ""considering the nuances and context"" - -Here are the mutanted parts: - -* ""The objective is to identify the type of"" instead of ""Please select the correct classification"" -* ""the provided questions"" instead of ""This sentence"" -* ""considering the nuances"" instead of ""taking into account the meaning and context"" - -3. Crossover the different parts with",0.2 -5,"Determine the categorization of the query by selecting one of the following options: Description, Entity, Expression, Human, Location, or Number, while thoroughly examining the question.",0.15 -5,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -5,"As a classification model, classify the input query into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -5,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -6,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -6,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.3 -6,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -6,"Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context",0.2 -6,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types"" - -Here are",0.25 -6,"Analyze the query and categorize it into one of the following categories: Description, Entity",0.2 -6,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -6,"As a classification model, classify the input query into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -6,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -7,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -7,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.3 -7,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Examine the prompts to identify the relevant type"" -""The dataset includes questions"" -> ""The questions are organized by category"" -""into one of the six categories"" -> ""from the following list"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types""",0.25 -7,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types"" - -Here are",0.25 -7,"Analyze the query and categorize it into one of the following categories: Description, Entity",0.2 -7,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -7,"The goal is to identify the nature of the inquiry based on a text passage, and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.35 -7,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -8,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -8,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.3 -8,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Examine the prompts to identify the relevant type"" -""The dataset includes questions"" -> ""The questions are organized by category"" -""into one of the six categories"" -> ""from the following list"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types""",0.25 -8,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types"" - -Here are",0.25 -8,"Analyze the query and categorize it into one of the following categories: Description, Entity",0.2 -8,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -8,"The goal is to identify the nature of the inquiry based on a text passage, and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.35 -8,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -9,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -9,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.3 -9,"Classify this sentence based on the categories in the following list, including Description, Entity, Expression, Human, Location, or Number,",0.2 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Examine the prompts to identify the relevant type"" -""The dataset includes questions"" -> ""The questions are organized by category"" -""into one of the six categories"" -> ""from the following list"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types""",0.25 -9,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types"" - -Here are",0.25 -9,"Analyze the query and categorize it into one of the following categories: Description, Entity",0.2 -9,"Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context.",0.25 -9,"The goal is to identify the nature of the inquiry based on a text passage, and categorize it into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.35 -9,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -10,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -10,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.35 -10,"Understand the sentence and identify the corresponding category of a question in the dataset, from the six options: Description, Entity, Expression, Human, Location, and Number.",0.25 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Examine the prompts to identify the relevant type"" -""The dataset includes questions"" -> ""The questions are organized by category"" -""into one of the six categories"" -> ""from the following list"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types""",0.25 -10,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types"" - -Here are",0.25 -10,"I'd be happy to help you generate a better prompt! Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate",0.5 -10,"Here are the instructions followed step-by-step: - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -**2. Randomly mutate the different parts:** - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types",0.4 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". -Prompt 2: Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context. - -Different parts: - -""You are given a question"" vs ""Classify each question"" -""detect which category better describes the question"" vs ""classify each question according to its type"" -""Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"""" - -2. Randomly mutate the different parts: - -""You are given a question"" -> ""Analyze a query"" -""detect which category better describes the question"" -> ""identify the nature of the inquiry"" -""Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"""" -> ""indicate one of the following categories: Description, Entity, Expression, Human, Location, or Number"" - -3. Crossover the different parts with",0.5 -10,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -11,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -11,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.35 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry. -Prompt 2: I'd be happy to help you generate a better prompt! Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different",0.3 -11,"Examine the prompts to identify the relevant type from the following list: Description, Entity, Expression, Human, Location, and Number, and predict the category mentioned first in the response, considering the context.",0.55 -11,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types"" - -Here are",0.25 -11,"I'd be happy to help you generate a better prompt! Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate",0.5 -11,"Here are the instructions followed step-by-step: - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -**2. Randomly mutate the different parts:** - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types",0.4 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". -Prompt 2: Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context. - -Different parts: - -""You are given a question"" vs ""Classify each question"" -""detect which category better describes the question"" vs ""classify each question according to its type"" -""Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"""" - -2. Randomly mutate the different parts: - -""You are given a question"" -> ""Analyze a query"" -""detect which category better describes the question"" -> ""identify the nature of the inquiry"" -""Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"""" -> ""indicate one of the following categories: Description, Entity, Expression, Human, Location, or Number"" - -3. Crossover the different parts with",0.5 -11,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 -12,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -12,"I'd be happy to help you generate a better prompt. Let's follow the instructions step-by-step. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate a",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry. -Prompt 2: I'd be happy to help you generate a better prompt! Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different",0.3 -12,"Examine the prompts to identify the relevant type from the following list: Description, Entity, Expression, Human, Location, and Number, and predict the category mentioned first in the response, considering the context.",0.55 -12,"I can help you with that. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: - -* ""Your task is to classify each question"" vs ""The dataset includes questions"" -* ""into one of the six categories"" vs ""categorized into different types"" -* ""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -2. Randomly mutate the different parts: - -""Your task is to classify each question"" -> ""Determine the type of inquiry"" -""The dataset includes questions"" -> ""The questions are received"" -""into one of the six categories"" -> ""from the given categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, Number"" -> ""the six categorized types"" - -Let me know if you'd",0.45 -12,"I'd be happy to help you generate a better prompt! Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Study the given inquiry and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Study the given inquiry and classify it"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""into one of the following categories: Description, Entity, Expression, Human, Location, or Number, considering the context"" - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""Determine the classification of the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""categories of Description, Entity, Expression, Human, Location, Number"" - -Let me know if you'd like me to generate more mutations! - -3. Crossover the different parts with Prompt 3 and generate",0.5 -12,"Here are the instructions followed step-by-step: - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Your task is to classify each question into one of the six categories: Description, Entity, Expression, Human, Location, and Number. -Prompt 2: The dataset includes questions categorized into different types: Description, Entity, Expression, Human, Location, and Number. The task is to classify each question into one of these six question types. The class mentioned first in the response of the LLM will be the prediction.. - -Different parts: -""Your task is to classify each question"" vs ""The dataset includes questions"" -""into one of the six categories"" vs ""categorized into different types"" -""The six categories"" vs ""Description, Entity, Expression, Human, Location, and Number"" - -**2. Randomly mutate the different parts:** - -""Your task is to classify each question"" -> ""Analyze the provided questions to determine the type"" -""The dataset includes questions"" -> ""The questions are categorized into different types"" -""into one of the six categories"" -> ""from the following categories"" -""The six categories"" -> ""Description, Entity, Expression, Human, Location, and Number"" -> ""the six distinct types",0.4 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". -Prompt 2: Classify each question according to its type: Description, Entity, Expression, Human, Location, or Number, and consider the meaning and context. - -Different parts: - -""You are given a question"" vs ""Classify each question"" -""detect which category better describes the question"" vs ""classify each question according to its type"" -""Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"""" - -2. Randomly mutate the different parts: - -""You are given a question"" -> ""Analyze a query"" -""detect which category better describes the question"" -> ""identify the nature of the inquiry"" -""Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"""" -> ""indicate one of the following categories: Description, Entity, Expression, Human, Location, or Number"" - -3. Crossover the different parts with",0.5 -12,"As a classification model, classify questions in the dataset and categorize them into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and context of each inquiry.",0.35 diff --git a/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 7a85a18..0000000 --- a/logs/experiment-task-descr/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,1154 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -1,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -1,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -1,"Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -1,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.1 -1,"As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.3 -1,"As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -2,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -2,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""Exercise the categories"" (avg. 6.6, 6.5) - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""Exercise the categories (avg. 6.6, 6.5)"" -> ""Tap into the given answers in order to determine the category"" - -So, the mutated parts are: - -""In this task, you will be acting as"" -""classify the given statement according to""",0.4 -2,The objective is to categorize these questions into one of the following categories: Description,0.2 -2,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""Classify the given question into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided question"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> "" categories: Description, Entity, Expression, Human",0.35 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -3,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -3,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -3,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.2 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""Exercise the categories"" (avg. 6.6, 6.5) - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""Exercise the categories (avg. 6.6, 6.5)"" -> ""Tap into the given answers in order to determine the category"" - -So, the mutated parts are: - -""In this task, you will be acting as"" -""classify the given statement according to""",0.4 -3,The objective is to categorize these questions into one of the following categories: Description,0.2 -3,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""Classify the given question into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided question"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> "" categories: Description, Entity, Expression, Human",0.35 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -4,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -4,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -4,"The goal is to determine the relevant category for this text, from the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -4,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.2 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""Exercise the categories"" (avg. 6.6, 6.5) - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""Exercise the categories (avg. 6.6, 6.5)"" -> ""Tap into the given answers in order to determine the category"" - -So, the mutated parts are: - -""In this task, you will be acting as"" -""classify the given statement according to""",0.4 -4,"Here's the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.25 -4,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""Classify the given question into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided question"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> "" categories: Description, Entity, Expression, Human",0.35 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -5,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -5,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: [Original text does not exist] - -(I assume you meant to provide Prompt 2, and I'll proceed accordingly) - -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""[No other differences found]"" - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -5,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you will categorize"" -""Given the question, assign a label"" -> ""Classify the provided text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location,",0.3 -5,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""this sentence"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""Return label only without any other text"" vs no mention of returning a label - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Classify each statement into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> """,0.25 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""Exercise the categories"" (avg. 6.6, 6.5) - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""Exercise the categories (avg. 6.6, 6.5)"" -> ""Tap into the given answers in order to determine the category"" - -So, the mutated parts are: - -""In this task, you will be acting as"" -""classify the given statement according to""",0.4 -5,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -5,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""Classify the given question into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided question"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> "" categories: Description, Entity, Expression, Human",0.35 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -6,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -6,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: [Original text does not exist] - -(I assume you meant to provide Prompt 2, and I'll proceed accordingly) - -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""[No other differences found]"" - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -6,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you will categorize"" -""Given the question, assign a label"" -> ""Classify the provided text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location,",0.3 -6,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""this sentence"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""Return label only without any other text"" vs no mention of returning a label - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Classify each statement into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> """,0.25 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""Exercise the categories"" (avg. 6.6, 6.5) - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""Exercise the categories (avg. 6.6, 6.5)"" -> ""Tap into the given answers in order to determine the category"" - -So, the mutated parts are: - -""In this task, you will be acting as"" -""classify the given statement according to""",0.4 -6,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -6,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""Classify the given question into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided question"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> "" categories: Description, Entity, Expression, Human",0.35 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -7,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.3 -7,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: [Original text does not exist] - -(I assume you meant to provide Prompt 2, and I'll proceed accordingly) - -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""[No other differences found]"" - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -7,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you will categorize"" -""Given the question, assign a label"" -> ""Classify the provided text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location,",0.3 -7,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""this sentence"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""Return label only without any other text"" vs no mention of returning a label - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""Classify each statement into one of the following categories"" -""Given the question, assign a label"" -> ""Analyze the provided text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> """,0.25 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: As a classifier, identify the category corresponding to these questions based on the categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: As a classification model, use the classification categories to analyze the given question and classify it into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""As a classifier"" vs ""As a classification model"" -""identify the category corresponding to these questions"" vs ""use the classification categories to analyze the given question and"" -""Exercise the categories"" (avg. 6.6, 6.5) - -2. Randomly mutate the different parts: - -""As a classifier"" -> ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""Exercise the categories (avg. 6.6, 6.5)"" -> ""Tap into the given answers in order to determine the category"" - -So, the mutated parts are: - -""In this task, you will be acting as"" -""classify the given statement according to""",0.4 -7,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -7,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -8,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the input text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number"" -""",0.4 -8,"Based on your instruction, I will follow the step-by-step process to generate a better prompt. - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Your task is to classify each question into one of these six question types: Description, Entity, Expression, Human, Location, and Number. The class mentioned first in the response of the LLM will be the prediction."" - -Different parts: -""Please perform Question Classification task"" vs ""Your task is to classify each question"" -""Given the question, assign a label"" vs ""into one of these six question types"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, and Number"" -""Return label only without any other text"" vs ""The class mentioned first in the response of the LLM will be the prediction."" -(no difference in ""this"" or ""no sentence"" as there is no mention of a sentence in either prompt) - -**2",0.35 -8,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.45 -8,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -8,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -9,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the input text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number"" -""",0.4 -9,"Based on your instruction, I will follow the step-by-step process to generate a better prompt. - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Your task is to classify each question into one of these six question types: Description, Entity, Expression, Human, Location, and Number. The class mentioned first in the response of the LLM will be the prediction."" - -Different parts: -""Please perform Question Classification task"" vs ""Your task is to classify each question"" -""Given the question, assign a label"" vs ""into one of these six question types"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, and Number"" -""Return label only without any other text"" vs ""The class mentioned first in the response of the LLM will be the prediction."" -(no difference in ""this"" or ""no sentence"" as there is no mention of a sentence in either prompt) - -**2",0.35 -9,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.45 -9,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -9,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -10,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the input text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number"" -""",0.4 -10,"Based on your instruction, I will follow the step-by-step process to generate a better prompt. - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Your task is to classify each question into one of these six question types: Description, Entity, Expression, Human, Location, and Number. The class mentioned first in the response of the LLM will be the prediction."" - -Different parts: -""Please perform Question Classification task"" vs ""Your task is to classify each question"" -""Given the question, assign a label"" vs ""into one of these six question types"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, and Number"" -""Return label only without any other text"" vs ""The class mentioned first in the response of the LLM will be the prediction."" -(no difference in ""this"" or ""no sentence"" as there is no mention of a sentence in either prompt) - -**2",0.35 -10,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.45 -10,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -10,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -11,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the input text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number"" -""",0.4 -11,"Based on your instruction, I will follow the step-by-step process to generate a better prompt. - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Your task is to classify each question into one of these six question types: Description, Entity, Expression, Human, Location, and Number. The class mentioned first in the response of the LLM will be the prediction."" - -Different parts: -""Please perform Question Classification task"" vs ""Your task is to classify each question"" -""Given the question, assign a label"" vs ""into one of these six question types"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, and Number"" -""Return label only without any other text"" vs ""The class mentioned first in the response of the LLM will be the prediction."" -(no difference in ""this"" or ""no sentence"" as there is no mention of a sentence in either prompt) - -**2",0.35 -11,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.45 -11,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -11,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""In this task, you will be acting as"" -""identify the category corresponding to these questions"" -> ""classify the given statement according to"" -""[No other differences found to mutate",0.3 -12,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -Prompt 2: ""Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number."" - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the input text"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number"" -""",0.4 -12,"Based on your instruction, I will follow the step-by-step process to generate a better prompt. - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: ""Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" - -Prompt 2: ""Your task is to classify each question into one of these six question types: Description, Entity, Expression, Human, Location, and Number. The class mentioned first in the response of the LLM will be the prediction."" - -Different parts: -""Please perform Question Classification task"" vs ""Your task is to classify each question"" -""Given the question, assign a label"" vs ""into one of these six question types"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""Description, Entity, Expression, Human, Location, and Number"" -""Return label only without any other text"" vs ""The class mentioned first in the response of the LLM will be the prediction."" -(no difference in ""this"" or ""no sentence"" as there is no mention of a sentence in either prompt) - -**2",0.35 -12,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.45 -12,"Here is the step-by-step guide to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Please perform Question Classification task"" vs ""Please pick the category that matches this sentence"" -""Given the question, assign a label"" vs ""..."" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" vs ""..."" -""Return label only without any other text"" vs ""..."" -""this sentence"" vs ""no sentence"" - -2. Randomly mutate the different parts: - -""Please perform Question Classification task"" -> ""In this task, you are required to classify"" -""Given the question, assign a label"" -> ""Classify the provided input"" -""['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -> ""categories: Description, Entity, Expression, Human, Location, Number""",0.3 -12,"Determine the type of the given inquiry based on the provided categories: Description, Entity, Expression, Human, Location, or Number. ""You are tasked to evaluate the statement"" -""phrase"" -> ""social media post"" -""with a positive or negative outlook, taking into account the context and sentiment"" -> ""according to its emotional tone and language cues"" -""based on the given sentence"" -> ""in the provided text"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt: -Prompt 3: When examining a user review, determine the sentiment",0.55 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -8,"Presented with a statement from feedback, assess the sentiment value and classify it as expressing a positive or negative opinion, evaluating the emotional aspect.",0.9 -8,"As a sentiment classifier, categorize the phrase with a positive or negative outlook, taking into account the context and sentiment, based on the given sentence.",0.9 -8,Classify a sentence as expressing optimism or pessimism.,0.85 -8,"In the role of a sentiment analyzer, examine a review excerpt and label the sentiment towards the product as either ""positive"" or ""negative"", identifying the sentiment as either supportive or critical",0.85 -8,"You are tasked to assess the sentiment of a phrase from product reviews, determining its emotional leaning as positive or negative, taking into account the overall sentiment and context.",0.9 -8,"As a sentiment classifier, evaluate a statement and determine the tone as favorable or unfavorable attitude, while considering the meaning and relevant context.",0.95 -8,"You need to determine the sentiment of the user feedback as favorable or unfavorable, let's follow the instructions step-by-step.",0.7 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -9,"Presented with a statement from feedback, assess the sentiment value and classify it as expressing a positive or negative opinion, evaluating the emotional aspect.",0.9 -9,"As a sentiment classifier, categorize the phrase with a positive or negative outlook, taking into account the context and sentiment, based on the given sentence.",0.9 -9,Classify a sentence as expressing optimism or pessimism.,0.85 -9,"In the role of a sentiment analyzer, examine a review excerpt and label the sentiment towards the product as either ""positive"" or ""negative"", identifying the sentiment as either supportive or critical",0.85 -9,"You are tasked to assess the sentiment of a phrase from product reviews, determining its emotional leaning as positive or negative, taking into account the overall sentiment and context.",0.9 -9,"As a sentiment classifier, evaluate a statement and determine the tone as favorable or unfavorable attitude, while considering the meaning and relevant context.",0.95 -9,"You need to determine the sentiment of the user feedback as favorable or unfavorable, let's follow the instructions step-by-step.",0.7 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -10,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -10,"Presented with a statement from feedback, assess the sentiment value and classify it as expressing a positive or negative opinion, evaluating the emotional aspect.",0.9 -10,"As a sentiment classifier, categorize the phrase with a positive or negative outlook, taking into account the context and sentiment, based on the given sentence.",0.9 -10,Classify a sentence as expressing optimism or pessimism.,0.85 -10,"In the role of a sentiment analyzer, examine a review excerpt and label the sentiment towards the product as either ""positive"" or ""negative"", identifying the sentiment as either supportive or critical",0.85 -10,"You are tasked to assess the sentiment of a phrase from product reviews, determining its emotional leaning as positive or negative, taking into account the overall sentiment and context.",0.9 -10,"As a sentiment classifier, evaluate a statement and determine the tone as favorable or unfavorable attitude, while considering the meaning and relevant context.",0.95 -10,"You need to determine the sentiment of the user feedback as favorable or unfavorable, let's follow the instructions step-by-step.",0.7 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -11,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -11,"Presented with a statement from feedback, assess the sentiment value and classify it as expressing a positive or negative opinion, evaluating the emotional aspect.",0.9 -11,"As a sentiment classifier, categorize the phrase with a positive or negative outlook, taking into account the context and sentiment, based on the given sentence.",0.9 -11,Classify a sentence as expressing optimism or pessimism.,0.85 -11,"In the role of a sentiment analyzer, examine a review excerpt and label the sentiment towards the product as either ""positive"" or ""negative"", identifying the sentiment as either supportive or critical",0.85 -11,"You are tasked to assess the sentiment of a phrase from product reviews, determining its emotional leaning as positive or negative, taking into account the overall sentiment and context.",0.9 -11,"As a sentiment classifier, evaluate a statement and determine the tone as favorable or unfavorable attitude, while considering the meaning and relevant context.",0.95 -11,"You need to determine the sentiment of the user feedback as favorable or unfavorable, let's follow the instructions step-by-step.",0.7 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -12,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -12,"Presented with a statement from feedback, assess the sentiment value and classify it as expressing a positive or negative opinion, evaluating the emotional aspect.",0.9 -12,"As a sentiment classifier, categorize the phrase with a positive or negative outlook, taking into account the context and sentiment, based on the given sentence.",0.9 -12,Classify a sentence as expressing optimism or pessimism.,0.85 -12,"In the role of a sentiment analyzer, examine a review excerpt and label the sentiment towards the product as either ""positive"" or ""negative"", identifying the sentiment as either supportive or critical",0.85 -12,"You are tasked to assess the sentiment of a phrase from product reviews, determining its emotional leaning as positive or negative, taking into account the overall sentiment and context.",0.9 -12,"As a sentiment classifier, evaluate a statement and determine the tone as favorable or unfavorable attitude, while considering the meaning and relevant context.",0.95 -12,"You need to determine the sentiment of the user feedback as favorable or unfavorable, let's follow the instructions step-by-step.",0.7 diff --git a/logs/experiment/cr_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv b/logs/experiment/cr_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv deleted file mode 100644 index 9c08d35..0000000 --- a/logs/experiment/cr_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv +++ /dev/null @@ -1,139 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.85 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -1,Analyze the tone of a user's feedback and classify it as positive or negative.,0.8 -1,"Determine the sentiment of a **post** based on a given text, classifying it as having a **favorable or unfavorable view**.",0.75 -1,"As a sentiment classifier, analyzing a piece of input",0.8 -1,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.5 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative. -Prompt 2: Determine the sentiment of a product review based on a given text, positive or negative. - -Different parts: -""You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context."" vs ""Determine the sentiment of a product review based on a given text"" -""sentence"" vs ""product review based on a given text"" -""classify it as positive or negative"" vs ""positive or negative"" - -2. Randomly mutate the different parts: - -""You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context."" -> ""Act as a sentiment analyzer and comprehend the text with its context."" -""sentence"" -> ""review text"" -""classify it as positive or negative"" -> ""label it as positive, negative"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed with ""Analyze the sentiment of sentences in reviews"" -* ""identify whether they express a positive or negative opinion"" -> ""classify the sentiment as positive or negative"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive",0.8 -10,Determine the sentiment of the text in a tweet and categorize it as having either positive or negative sentiment.,0.85 -10,The goal is to evaluate the review's tone and then categorize it as expressing a positive or negative tone while reviewing a product.,0.9 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -11,"As a sentiment analyzer, analyze text snippets and determine the sentiment, categorizing it based on the positive or negative tone.",0.9 -11,"Given a statement, classify it as expressing a positive or negative opinion.",0.8 -11,"Considered in the context of consumer feedback, analyze a consumer feedback and classify it as a definite perspective of having a positive or negative opinion",0.9 -11,"Analyze the text and identify the sentiment, determining whether it conveys a positive/negative opinion while considering the meaning and context of the sentence.",0.9 -11,"Determine the sentiment of the utterances in passages and identify whether they express a positive or negative opinion, taking into account the emotional tone, meaning, and context.",0.9 -11,"As a sentiment analyzer, determine the sentiment of a passage in a text, considering the situation and context, and categorize it as optimistic or pessimistic.",0.8 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Determine the sentiment of the utterances in passages and identify whether they express a positive or negative opinion, taking into account the emotional tone, meaning, and context. -Prompt 2: As a sentiment analyzer, analyze text snippets and determine the sentiment, categorizing it based on the positive or negative tone. - -Different parts: - -* ""Determine the sentiment of the utterances in passages"" vs ""analyze text snippets"" -* ""identify whether they express a positive or negative opinion"" vs ""determine the sentiment, categorizing it based on the positive or negative tone"" - -**Step 2: Randomly mutate the different parts** - -* ""Determine the sentiment of the utterances in passages"" -> ""Analyze the sentiment of sentences in reviews"" -* ""identify whether they express a positive or negative opinion"" -> ""classify the sentiment as positive or negative"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive",0.8 -11,"As a sentiment analyzer, examine language inputs and classify a consumer response into either a positive or negative perspective, taking into account the context and sentiment.",0.9 -11,The goal is to evaluate the review's tone and then categorize it as expressing a positive or negative tone while reviewing a product.,0.9 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -12,"As a sentiment analyzer, analyze text snippets and determine the sentiment, categorizing it based on the positive or negative tone.",0.9 -12,"Given a statement, classify it as expressing a positive or negative opinion.",0.8 -12,"Considered in the context of consumer feedback, analyze a consumer feedback and classify it as a definite perspective of having a positive or negative opinion",0.9 -12,"Analyze the text and identify the sentiment, determining whether it conveys a positive/negative opinion while considering the meaning and context of the sentence.",0.9 -12,"Determine the sentiment of the utterances in passages and identify whether they express a positive or negative opinion, taking into account the emotional tone, meaning, and context.",0.9 -12,"As a sentiment analyzer, determine the sentiment of a passage in a text, considering the situation and context, and categorize it as optimistic or pessimistic.",0.8 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Determine the sentiment of the utterances in passages and identify whether they express a positive or negative opinion, taking into account the emotional tone, meaning, and context. -Prompt 2: As a sentiment analyzer, analyze text snippets and determine the sentiment, categorizing it based on the positive or negative tone. - -Different parts: - -* ""Determine the sentiment of the utterances in passages"" vs ""analyze text snippets"" -* ""identify whether they express a positive or negative opinion"" vs ""determine the sentiment, categorizing it based on the positive or negative tone"" - -**Step 2: Randomly mutate the different parts** - -* ""Determine the sentiment of the utterances in passages"" -> ""Analyze the sentiment of sentences in reviews"" -* ""identify whether they express a positive or negative opinion"" -> ""classify the sentiment as positive or negative"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive",0.8 -12,"As a sentiment analyzer, examine language inputs and classify a consumer response into either a positive or negative perspective, taking into account the context and sentiment.",0.9 -12,The goal is to evaluate the review's tone and then categorize it as expressing a positive or negative tone while reviewing a product.,0.9 diff --git a/logs/experiment/cr_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/cr_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index fe293ae..0000000 --- a/logs/experiment/cr_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.85 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -1,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.65 -1,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -1,"Determine the sentiment of the text provided, positve or negative.",0.65 -1,"The objective is to assess the emotional tone of the provided passage as it relates to a product review, and categorize it as positive or negative.",0.65 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -2,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -2,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -2,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -2,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.65 -2,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -2,"Determine the sentiment of the text provided, positve or negative.",0.65 -2,The goal is to evaluate the emotional tone of a comment and output the sentiment classification as one of the labels of sentiment in relation to a product review.,0.7 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -3,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -3,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -3,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -3,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -3,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.65 -3,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -3,"Determine the sentiment of the text provided, positve or negative.",0.65 -3,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -4,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -4,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -4,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -4,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -4,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.65 -4,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -4,"Determine the sentiment of the text provided, positve or negative.",0.65 -4,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -5,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -5,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -5,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -5,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -5,"Examine the text and determine the emotion conveyed in the sentiment classification, whether as a positive sentiment or negative opinion.",0.7 -5,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -5,"Determine the sentiment of the text provided, positve or negative.",0.65 -5,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -6,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -6,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -6,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -6,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -6,"Examine the text and determine the emotion conveyed in the sentiment classification, whether as a positive sentiment or negative opinion.",0.7 -6,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -6,"Determine the sentiment of the text provided, positve or negative.",0.65 -6,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -7,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -7,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -7,"As you read the passage, identify the tone and sentiment, classifying the sentiment as having a positive or negative tone.",0.9 -7,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -7,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -7,"Examine the text and determine the emotion conveyed in the sentiment classification, whether as a positive sentiment or negative opinion.",0.7 -7,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -7,"Determine the tone of the extracted data and sentiment expressed in the statement between positive and negative viewpoints, outputting sentiment classification as favorable or unfavorable inclination.",0.8 -7,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -8,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -8,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -8,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -8,"As you read the passage, identify the tone and sentiment, classifying the sentiment as having a positive or negative tone.",0.9 -8,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -8,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -8,"Examine a passage and determine the emotions conveyed in the sentiment analysis,",0.75 -8,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -8,"Determine the tone of the extracted data and sentiment expressed in the statement between positive and negative viewpoints, outputting sentiment classification as favorable or unfavorable inclination.",0.8 -8,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -9,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -9,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -9,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -9,"As you read the passage, identify the tone and sentiment, classifying the sentiment as having a positive or negative tone.",0.9 -9,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -9,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -9,"Examine a passage and determine the emotions conveyed in the sentiment analysis,",0.75 -9,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -9,"Determine the tone of the extracted data and sentiment expressed in the statement between positive and negative viewpoints, outputting sentiment classification as favorable or unfavorable inclination.",0.8 -9,"As a classifier, you are responsible for evaluating the emotional tone of the text and outputting sentiment classification as positive sentiment or a negative opinion.",0.9 -10,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -10,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -10,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -10,"As you read the passage, identify the tone and sentiment, classifying the sentiment as having a positive or negative tone.",0.9 -10,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -10,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -10,"Examine a passage and determine the emotions conveyed in the sentiment analysis,",0.75 -10,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -10,"Determine the tone of the extracted data and sentiment expressed in the statement between positive and negative viewpoints, outputting sentiment classification as favorable or unfavorable inclination.",0.8 -10,"Examine the statement and determine the emotional resonance of the text, evaluating whether it belongs to positive sentiment or a negative opinion.",1.0 -11,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -11,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -11,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -11,"As you read the passage, identify the tone and sentiment, classifying the sentiment as having a positive or negative tone.",0.9 -11,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -11,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -11,"Examine a passage and determine the emotions conveyed in the sentiment analysis,",0.75 -11,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -11,"Determine the tone of the extracted data and sentiment expressed in the statement between positive and negative viewpoints, outputting sentiment classification as favorable or unfavorable inclination.",0.8 -11,"Examine the statement and determine the emotional resonance of the text, evaluating whether it belongs to positive sentiment or a negative opinion.",1.0 -12,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -12,"Examine the text, which can have either a positive or negative connotation, and assign a sentiment label from ['negative', 'positive'] from the given sentence, without providing any additional context.",0.95 -12,"As your task, observe the phrase and determine its sentiment concrete, identifying whether the text corresponds to positive or negative sentiment.",0.9 -12,"As you read the passage, identify the tone and sentiment, classifying the sentiment as having a positive or negative tone.",0.9 -12,"Categorize the emotional tone of the statement, identifying emotions expressed and classifying it as expressing a positive or negative opinion.",0.9 -12,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -12,"Examine a passage and determine the emotions conveyed in the sentiment analysis,",0.75 -12,"Examine the message to determine its emotional polarity, identifying whether the text corresponds to positive or negative sentiment.",0.9 -12,"Determine the tone of the extracted data and sentiment expressed in the statement between positive and negative viewpoints, outputting sentiment classification as favorable or unfavorable inclination.",0.8 -12,"Examine the statement and determine the emotional resonance of the text, evaluating whether it belongs to positive sentiment or a negative opinion.",1.0 diff --git a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv b/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv deleted file mode 100644 index 0c56deb..0000000 --- a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,"Determine the sentiment of a given sentence, considering its meaning and context, and label it as either positive or negative.",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -1,"Determine the emotional tone of the input sentence, categorizing it as positive, negative, optimistic, or pessimistic.",0.85 -1,Classify a sentence as expressing optimism or pessimism.,0.8 -1,Label the provided sentence as 'positive' or 'negative' based on its sentiment.,0.75 -1,Assign a sentiment label of 'positive' or 'negative' to the provided text.,0.75 -2,"Classify the sentiment of the provided text as either 'positive' or 'negative', returning only the label.",0.95 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -2,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.9 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -2,"Determine the sentiment of a given sentence, considering its meaning and context, and label it as either positive or negative.",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -2,"Determine the sentiment of the provided text, categorizing it as either 'positive' or 'negative'.",0.85 -2,"Determine the sentiment of a given sentence, categorizing it as either optimistic or pessimistic.",0.85 -2,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"".",0.85 -3,"Classify the sentiment of the provided text as either 'positive' or 'negative', returning only the label.",0.95 -3,"Classify the sentiment of the provided product review, choosing either ""positive"" or ""negative"" as the label.",0.95 -3,"Classify the sentiment expressed in the provided review or comment as either ""positive"" or ""negative"" in relation to the product.",0.95 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -3,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.9 -3,"Label the sentiment of the provided text as either ""positive"" or ""negative"", indicating its emotional tone.",0.9 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -3,"Determine the sentiment of a given sentence, considering its meaning and context, and label it as either positive or negative.",0.9 -3,"Classify the sentiment of the provided text, labeling it as either 'positive' or 'negative'.",0.9 -4,Determine the emotional tone of the provided text and categorize it as positive or negative sentiment.,0.95 -4,"Classify the sentiment of the provided text as either 'positive' or 'negative', returning only the label.",0.95 -4,"Classify the sentiment of the provided product review, choosing either ""positive"" or ""negative"" as the label.",0.95 -4,"Classify the sentiment expressed in the provided review or comment as either ""positive"" or ""negative"" in relation to the product.",0.95 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -4,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.9 -4,"Label the sentiment of the provided text as either ""positive"" or ""negative"", indicating its emotional tone.",0.9 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -4,Determine whether the given comment expresses a positive or negative sentiment.,0.9 -5,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -5,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -5,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -5,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -5,"Determine the sentiment of a provided text, selecting either ""positive"" or ""negative"" as the classification.",0.95 -5,Determine the emotional tone of the provided text and categorize it as positive or negative sentiment.,0.95 -5,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -5,"Classify the sentiment of the provided text as either 'positive' or 'negative', returning only the label.",0.95 -5,"Classify the sentiment of the provided product review, choosing either ""positive"" or ""negative"" as the label.",0.95 -5,"Classify the sentiment expressed in the provided review or comment as either ""positive"" or ""negative"" in relation to the product.",0.95 -6,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -6,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -6,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -6,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -6,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -6,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -6,"Identify the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional expression.",0.95 -6,"Determine the sentiment of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -6,"Determine the sentiment of a provided text, selecting either ""positive"" or ""negative"" as the classification.",0.95 -6,Determine the emotional tone of the provided text and categorize it as positive or negative sentiment.,0.95 -7,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -7,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -7,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -7,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -7,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -7,"Analyze the provided review and classify its sentiment as either ""positive"" or ""negative"" reflecting the author's emotional tone.",1.0 -7,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -7,"Identify the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional expression.",0.95 -7,"Identify the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment to understand its attitude towards a product or service.",0.95 -7,"Determine the sentiment of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -8,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -8,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -8,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -8,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -8,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -8,"Analyze the provided review and classify its sentiment as either ""positive"" or ""negative"" reflecting the author's emotional tone.",1.0 -8,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -8,"Identify the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional expression.",0.95 -8,"Identify the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment to understand its attitude towards a product or service.",0.95 -8,"Determine the sentiment of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -9,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -9,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -9,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -9,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -9,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -9,"Analyze the provided review and classify its sentiment as either ""positive"" or ""negative"" reflecting the author's emotional tone.",1.0 -9,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -9,"Identify the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional expression.",0.95 -9,"Identify the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment to understand its attitude towards a product or service.",0.95 -9,"Determine the sentiment of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -10,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -10,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -10,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -10,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -10,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -10,"Analyze the provided review and classify its sentiment as either ""positive"" or ""negative"" reflecting the author's emotional tone.",1.0 -10,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -10,"Identify the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional expression.",0.95 -10,"Identify the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment to understand its attitude towards a product or service.",0.95 -10,"Determine the sentiment of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -11,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -11,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -11,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -11,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -11,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -11,"Analyze the provided review and classify its sentiment as either ""positive"" or ""negative"" reflecting the author's emotional tone.",1.0 -11,"Analyze the given text to determine its sentiment as either ""positive"" or ""negative"", reflecting its emotional standpoint towards the product or service.",1.0 -11,"Identify the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional expression.",0.95 -11,"Identify the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment to understand its attitude towards a product or service.",0.95 -11,"Determine the sentiment of the given text, categorizing it as either ""positive"" or ""negative"".",0.95 -12,"Label the provided sentence with either ""positive"" or ""negative"" sentiment.",1.0 -12,"Determine whether the sentiment expressed in the given review is ""positive"" or ""negative"" towards the product.",1.0 -12,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -12,"Determine the emotional tone of the given text, classifying it as either ""positive"" or ""negative"" sentiment.",1.0 -12,"Determine the emotional tone of the given text, categorizing it as either ""positive"" or ""negative"" in terms of its sentiment towards the product or service.",1.0 -12,"Classify the sentiment of the provided text as either ""positive"" or ""negative"", accordingly.",1.0 -12,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",1.0 -12,"Classify the emotional tone of the provided text, categorizing it as ""positive"" or ""negative"" sentiment.",1.0 -12,"Analyze the provided review and classify its sentiment as either ""positive"" or ""negative"" reflecting the author's emotional tone.",1.0 -12,"Analyze the given text to identify its emotional tone, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 diff --git a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv b/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv deleted file mode 100644 index 4d30d56..0000000 --- a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Determine the sentiment of a provided sentence, categorizing it as either positive or negative.",0.9 -1,"Determine the sentiment of a provided sentence, categorizing it as either 'positive' or 'negative'.",0.9 -1,"Analyze the given sentence, taking into account its meaning and any relevant context, and categorize its sentiment as either positive or negative.",0.9 -1,"In this task, you are given sentences from product reviews. The task is to classify a sentence as positive or as negative.",0.85 -1,"Determine whether the provided statement conveys a positive or negative sentiment, and label it accordingly.",0.85 -1,"Determine the sentiment of a provided text, categorizing it as either positive or negative.",0.85 -2,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -2,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -2,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional tone.",0.95 -2,Classify the given sentence or text as having a 'positive' or 'negative' sentiment.,0.95 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -2,"Determine the sentiment of a provided sentence, categorizing it as either positive or negative.",0.9 -2,"Determine the sentiment of a provided sentence, categorizing it as either 'positive' or 'negative'.",0.9 -2,"Analyze the given sentence, taking into account its meaning and any relevant context, and categorize its sentiment as either positive or negative.",0.9 -3,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -3,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -3,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -3,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional tone.",0.95 -3,Classify the given sentence or text as having a 'positive' or 'negative' sentiment.,0.95 -3,"Assess the sentiment of a given sentence, taking context into account, and classify it as either positive or negative.",0.95 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -3,"Determine the sentiment of a provided sentence, categorizing it as either positive or negative.",0.9 -4,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -4,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -4,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -4,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -4,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -4,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional tone.",0.95 -4,Classify the given sentence or text as having a 'positive' or 'negative' sentiment.,0.95 -4,"Assess the sentiment of a given sentence, taking context into account, and classify it as either positive or negative.",0.95 -5,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -5,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -5,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -5,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -5,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -5,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -5,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -5,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -6,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -6,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -6,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -6,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -6,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -6,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -6,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -6,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -7,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -7,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -7,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -7,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -7,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -7,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -7,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -7,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -8,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -8,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -8,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -8,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -8,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -8,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -8,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -8,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -9,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -9,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -9,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -9,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -9,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -9,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -9,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -9,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -10,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -10,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -10,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -10,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -10,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -10,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -10,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -10,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -11,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -11,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -11,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -11,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -11,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -11,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -11,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -11,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 -12,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -12,"Determine the sentiment of the provided comment, labeling it as either ""positive"" or ""negative"" sentiment.",1.0 -12,Classify the sentiment of the provided text as positive or negative by evaluating its emotional tone and language.,1.0 -12,"Assess the emotional tone of the provided text and label it as either ""positive"" or ""negative"" sentiment.",1.0 -12,"Analyze the emotional tone of the given text to classify its sentiment as either ""positive"" or ""negative"".",1.0 -12,"Analyze the emotional tone of the given text and classify its sentiment as either ""positive"" or ""negative"".",1.0 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Please label the sentiment towards the product of the given product review. The sentiment label should be ""positive"" or ""negative"".",0.95 -12,"Identify and categorize the sentiment of a provided sentence, labelling it as either positive or negative.",0.95 -12,Evaluate the context-dependent sentiment of a sentence and label it as either positive or negative.,0.95 diff --git a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv b/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv deleted file mode 100644 index a5078b1..0000000 --- a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -1,"Determine the sentiment of the provided comment, categorizing it as either ""positive"" or ""negative"".",0.9 -1,"Analyze the provided sentence, considering its meaning and context, and categorize it as either positive or negative in sentiment.",0.9 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -1,Determine the sentiment of a given text as positive or negative.,0.8 -1,"Determine the sentiment of a given statement as either positive, negative, or neutral.",0.8 -2,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -2,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -2,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -2,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -2,"Determine the sentiment of the provided sentence, taking into account its context and meaning, and classify it as either 'negative' or 'positive'.",0.9 -2,"Determine the sentiment of the provided comment, categorizing it as either ""positive"" or ""negative"".",0.9 -2,"Determine the sentiment of a provided statement, categorizing it as positive or negative.",0.9 -3,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -3,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -3,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -3,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -3,"Determine the sentiment of the provided sentence, taking into account its context and meaning, and classify it as either 'negative' or 'positive'.",0.9 -3,"Determine the sentiment of the provided comment, categorizing it as either ""positive"" or ""negative"".",0.9 -3,"Determine the sentiment of a provided statement, categorizing it as positive or negative.",0.9 -4,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -4,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -4,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -4,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -4,"Determine the sentiment of the provided sentence, taking into account its context and meaning, and classify it as either 'negative' or 'positive'.",0.9 -4,"Determine the sentiment of the provided comment, categorizing it as either ""positive"" or ""negative"".",0.9 -4,"Determine the sentiment of a provided statement, categorizing it as positive or negative.",0.9 -5,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -5,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -5,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -5,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -5,"Determine the sentiment of the provided sentence, taking into account its context and meaning, and classify it as either 'negative' or 'positive'.",0.9 -5,"Determine the sentiment of the provided comment, categorizing it as either ""positive"" or ""negative"".",0.9 -5,"Determine the sentiment of a provided statement, categorizing it as positive or negative.",0.9 -6,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -6,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -6,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -6,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -6,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -6,"Determine the sentiment of the provided sentence, taking into account its context and meaning, and classify it as either 'negative' or 'positive'.",0.9 -6,"Determine the sentiment of the provided comment, categorizing it as either ""positive"" or ""negative"".",0.9 -7,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -7,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -7,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -7,"Analyze the given text in context and assign a sentiment label of ""positive"" or ""negative"" based on its emotional tone.",0.95 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -7,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -7,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -7,"Determine the sentiment of the provided sentence, taking into account its context and meaning, and classify it as either 'negative' or 'positive'.",0.9 -8,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -8,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -8,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -8,"Analyze the provided text within its context to categorize its sentiment as either ""positive"" or ""negative"".",0.95 -8,"Analyze the given text to identify its sentiment, taking context into account, and label it as either ""positive"" or ""negative"".",0.95 -8,"Analyze the given text in context and assign a sentiment label of ""positive"" or ""negative"" based on its emotional tone.",0.95 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -8,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -8,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -9,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -9,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -9,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -9,"Analyze the provided text within its context to categorize its sentiment as either ""positive"" or ""negative"".",0.95 -9,"Analyze the given text to identify its sentiment, taking context into account, and label it as either ""positive"" or ""negative"".",0.95 -9,"Analyze the given text in context and assign a sentiment label of ""positive"" or ""negative"" based on its emotional tone.",0.95 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -9,Label the provided statement with either 'positive' or 'negative' based on its sentiment.,0.9 -9,"Interpret the provided text, taking into account its meaning and context, and categorize its sentiment as either positive or negative.",0.9 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -10,"Label the provided sentence as either ""positive"" or ""negative"" based on its sentiment.",0.95 -10,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -10,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -10,"Determine the emotional tone of the provided text and categorize it as either ""positive"" or ""negative"" sentiment.",0.95 -10,"Determine the emotional tone of the given text, categorizing it as either 'positive' or 'negative'.",0.95 -10,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -10,"Analyze the provided text within its context to categorize its sentiment as either ""positive"" or ""negative"".",0.95 -10,"Analyze the given text to identify its sentiment, taking context into account, and label it as either ""positive"" or ""negative"".",0.95 -10,"Analyze the given text in context and assign a sentiment label of ""positive"" or ""negative"" based on its emotional tone.",0.95 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -11,"Classify the sentiment of the provided sentence as either ""positive"" or ""negative"".",1.0 -11,"Label the provided sentence as either ""positive"" or ""negative"" based on its sentiment.",0.95 -11,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -11,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -11,"Determine the emotional tone of the provided text and categorize it as either ""positive"" or ""negative"" sentiment.",0.95 -11,"Determine the emotional tone of the given text, categorizing it as either 'positive' or 'negative'.",0.95 -11,"Classify the provided text as ""positive"" or ""negative"" based on its contextual emotional tone and sentiment.",0.95 -11,"Classify the emotional tone of the provided text as ""positive"" or ""negative"" sentiment after thorough analysis.",0.95 -11,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -11,"Analyze the provided text within its context to categorize its sentiment as either ""positive"" or ""negative"".",0.95 -12,"Classify the sentiment of the provided sentence as either ""positive"" or ""negative"".",1.0 -12,"Label the provided sentence as either ""positive"" or ""negative"" based on its sentiment.",0.95 -12,"Evaluate the provided text in its context to label its sentiment as either ""positive"" or ""negative"".",0.95 -12,"Determine the sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -12,"Determine the emotional tone of the provided text and categorize it as either ""positive"" or ""negative"" sentiment.",0.95 -12,"Determine the emotional tone of the given text, categorizing it as either 'positive' or 'negative'.",0.95 -12,"Classify the provided text as ""positive"" or ""negative"" based on its contextual emotional tone and sentiment.",0.95 -12,"Classify the emotional tone of the provided text as ""positive"" or ""negative"" sentiment after thorough analysis.",0.95 -12,"Assign a sentiment label, either ""positive"" or ""negative"", to the provided sentence.",0.95 -12,"Analyze the provided text within its context to categorize its sentiment as either ""positive"" or ""negative"".",0.95 diff --git a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index fdfcb53..0000000 --- a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,Classify a sentence as expressing optimism or pessimism.,0.95 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -1,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.85 -1,"Given text, classify whether it is positive or negative.",0.85 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -1,"Assess the emotional tone of the text, categorizing it as either positive or negative.",0.8 -1,"Classify the given text as either positive or negative, taking into account its meaning and relevant context.",0.7 -1,Analyze and classify the given comment as positive or negative by understanding its meaning and context.,0.7 -2,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -2,Classify a sentence as expressing optimism or pessimism.,0.95 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -2,"Evaluate the provided text and categorize it as either positive or negative, considering its semantic tone and relevant context.",0.9 -2,"Classify and assign a sentiment label ('negative' or 'positive') to the input text, considering its meaning and context.",0.9 -2,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.85 -2,"Given text, classify whether it is positive or negative.",0.85 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -2,"Classify the comment's sentiment as ""positive"" or ""negative"" by understanding its meaning within the given context.",0.85 -3,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -3,Classify a sentence as expressing optimism or pessimism.,0.95 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -3,"Evaluate the provided text and categorize it as either positive or negative, considering its semantic tone and relevant context.",0.9 -3,Classify the provided text into 'positive' or 'negative' sentiment and return the corresponding label.,0.9 -3,"Classify and assign a sentiment label ('negative' or 'positive') to the input text, considering its meaning and context.",0.9 -3,"Assess the emotional tone of the input text, categorizing it as optimistic or pessimistic, or alternatively, as having a positive or negative sentiment.",0.9 -3,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.85 -3,"Identify the sentiment of the provided comment, distinguishing between ""positive"" or ""negative"" and take into account the context and tone.",0.85 -4,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -4,Classify a sentence as expressing optimism or pessimism.,0.95 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -4,"Evaluate the provided text and categorize it as either positive or negative, considering its semantic tone and relevant context.",0.9 -4,"Determine the sentiment of the given text by categorizing it as ""positive"" or ""negative"", with due consideration for the context and tone.",0.9 -4,Classify the provided text into 'positive' or 'negative' sentiment and return the corresponding label.,0.9 -4,"Classify and assign a sentiment label ('negative' or 'positive') to the input text, considering its meaning and context.",0.9 -4,"Assess the emotional tone of the input text, categorizing it as optimistic or pessimistic, or alternatively, as having a positive or negative sentiment.",0.9 -4,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.85 -5,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -5,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -5,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -5,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -5,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -5,Classify a sentence as expressing optimism or pessimism.,0.95 -5,"Classify a given text and label it as either 'negative' or 'positive', taking into account its meaning and context.",0.95 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.9 -5,"Evaluate the provided text and categorize it as either positive or negative, considering its semantic tone and relevant context.",0.9 -6,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -6,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -6,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -6,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -6,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -6,Classify the text's sentiment as either 'positive' or 'negative' based on its tone and relevant context,0.95 -6,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -6,Classify a sentence as expressing optimism or pessimism.,0.95 -6,"Classify a given text and label it as either 'negative' or 'positive', taking into account its meaning and context.",0.95 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -7,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -7,"Determine the sentiment of the text ('positive' or 'negative') considering its meaning, context, and emotional tone.",1.0 -7,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -7,"Analyze the input text to determine its sentiment, providing a nuanced assessment of its emotional tone as 'positive' or 'negative', taking into account context and subtleties.",1.0 -7,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -7,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -7,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -7,Classify the text's sentiment as either 'positive' or 'negative' based on its tone and relevant context,0.95 -7,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -7,Classify a sentence as expressing optimism or pessimism.,0.95 -8,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -8,"Determine the sentiment of the text, categorizing it as 'positive' or 'negative', taking into account its tone, context, and underlying nuances.",1.0 -8,"Determine the sentiment of the text ('positive' or 'negative') considering its meaning, context, and emotional tone.",1.0 -8,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -8,"Analyze the input text to determine its sentiment, providing a nuanced assessment of its emotional tone as 'positive' or 'negative', taking into account context and subtleties.",1.0 -8,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -8,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -8,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -8,Classify the text's sentiment as either 'positive' or 'negative' based on its tone and relevant context,0.95 -8,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -9,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -9,"Determine the sentiment of the text, categorizing it as 'positive' or 'negative', taking into account its tone, context, and underlying nuances.",1.0 -9,"Determine the sentiment of the text ('positive' or 'negative') considering its meaning, context, and emotional tone.",1.0 -9,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -9,"Analyze the input text to determine its sentiment, providing a nuanced assessment of its emotional tone as 'positive' or 'negative', taking into account context and subtleties.",1.0 -9,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -9,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -9,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -9,Classify the text's sentiment as either 'positive' or 'negative' based on its tone and relevant context,0.95 -9,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -10,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -10,"Determine the sentiment of the text, categorizing it as 'positive' or 'negative', taking into account its tone, context, and underlying nuances.",1.0 -10,"Determine the sentiment of the text ('positive' or 'negative') considering its meaning, context, and emotional tone.",1.0 -10,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -10,"Analyze the input text to determine its sentiment, providing a nuanced assessment of its emotional tone as 'positive' or 'negative', taking into account context and subtleties.",1.0 -10,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -10,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -10,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -10,Classify the text's sentiment as either 'positive' or 'negative' based on its tone and relevant context,0.95 -10,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -11,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -11,"Determine the sentiment of the text, categorizing it as 'positive' or 'negative', taking into account its tone, context, and underlying nuances.",1.0 -11,"Determine the sentiment of the text ('positive' or 'negative') considering its meaning, context, and emotional tone.",1.0 -11,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -11,"Analyze the input text to determine its sentiment, providing a nuanced assessment of its emotional tone as 'positive' or 'negative', taking into account context and subtleties.",1.0 -11,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -11,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -11,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 -11,Classify the text's sentiment as either 'positive' or 'negative' based on its tone and relevant context,0.95 -11,Classify the provided text (sentence or tweet) as 'negative' or 'positive' in terms of its sentiment.,0.95 -12,"Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.",1.0 -12,"Determine the sentiment of the text, categorizing it as 'positive' or 'negative', taking into account its tone, context, and underlying nuances.",1.0 -12,"Determine the sentiment of the text ('positive' or 'negative') considering its meaning, context, and emotional tone.",1.0 -12,"Classify the text into a sentiment label ('positive' or 'negative'), considering its meaning and context",1.0 -12,"Assess the sentiment of the given text, labeling it as 'positive', 'negative', or 'neutral', taking into account its linguistic subtleties, contextual relevance, and emotional undertones.",1.0 -12,"Analyze the input text to determine its sentiment, providing a nuanced assessment of its emotional tone as 'positive' or 'negative', taking into account context and subtleties.",1.0 -12,"Identify the sentiment of the provided text, categorizing it as 'positive' or 'negative', whilst taking into account its context and underlying meaning.",0.95 -12,"Heapily categorize the provided text as ""positive"" or ""negative"", weighing the importance of context and semantic tone in your assessment",0.95 -12,"Determine the sentiment of the input text, identifying its emotional tone as 'positive', 'negative', or 'nuanced', considering the complexities of the text and its context.",0.95 -12,Detect the sentiment of the input text and categorize it as 'positive' or 'negative',0.95 diff --git a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index f89f6f1..0000000 --- a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.85 -1,"Assess the sentiment of a sentence, categorizing it as either optimistic, pessimistic, or neutral.",0.85 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.8 -1,Assess the provided sentence as either optimistic or possessing a positive sentiment.,0.8 -1,"Assess the emotional tone of a given sentence, categorizing it as positive, negative, or neutral.",0.8 -1,"Analyze the sentiment of a given text, classifying it as either positive or negative.",0.8 -1,"Identify the sentiment of the product review as either ""positive"" or ""negative"" by deciphering its meaning and context.",0.75 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.7 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -2,"Sentiment Analysis Task: Classify the input sentence as either 'negative' or 'positive', indicating its emotional tone.",0.85 -2,Perform sentiment analysis on the input text and assign a sentiment label ('positive' or 'negative').,0.85 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.85 -2,"Assess the sentiment of a sentence, categorizing it as either optimistic, pessimistic, or neutral.",0.85 -2,"Analyze and categorize the provided statement or comment as holding either a positive or negative opinion, ensuring accuracy.",0.85 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.8 -2,Assess the provided sentence as either optimistic or possessing a positive sentiment.,0.8 -2,"Assess the emotional tone of a given sentence, categorizing it as positive, negative, or neutral.",0.8 -3,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -3,"Classify the provided statement as conveying either an opinion or sentiment, and determine its undertone as positive, negative, or neutral.",0.9 -3,Classify the input sentence as 'positive' or 'negative' according to its emotional tone and return the corresponding sentiment label.,0.9 -3,"Classify a sentence as positive, negative, optimistic, pessimistic, or neutral.",0.9 -3,"Analyze the provided text to identify its sentiment as positive or negative, returning the appropriate label.",0.9 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -3,"Sentiment Analysis Task: Classify the input sentence as either 'negative' or 'positive', indicating its emotional tone.",0.85 -3,Perform sentiment analysis on the input text and assign a sentiment label ('positive' or 'negative').,0.85 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.85 -4,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -4,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -4,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -4,"Determine the emotional tone of the input text and label it as either 'positive' or 'negative', effectively identifying its sentiment.",0.9 -4,"Classify the provided statement as conveying either an opinion or sentiment, and determine its undertone as positive, negative, or neutral.",0.9 -4,Classify the input sentence as 'positive' or 'negative' according to its emotional tone and return the corresponding sentiment label.,0.9 -4,"Classify a sentence as positive, negative, optimistic, pessimistic, or neutral.",0.9 -4,"Analyze the provided text to identify its sentiment as positive or negative, returning the appropriate label.",0.9 -4,"Analyze the given text to identify its sentiment by classifying it as either 'positive', 'negative', or 'neutral', while also determining the emotional tone it conveys.",0.9 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -5,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -5,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -5,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -5,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -5,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -5,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -5,"Identify the sentiment and emotional tone of the provided text, categorizing it as 'positive', 'negative', or 'neutral', and provide a detailed analysis of its emotional resonance",0.9 -5,"Evaluate the sentiment of the provided text, labeling it as positive, negative, optimistic, pessimistic, or neutral while considering its tone and emotional connotation.",0.9 -5,"Determine the emotional tone of the input text and label it as either 'positive' or 'negative', effectively identifying its sentiment.",0.9 -5,"Classify the provided statement as conveying either an opinion or sentiment, and determine its undertone as positive, negative, or neutral.",0.9 -6,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -6,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -6,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -6,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -6,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -6,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -6,"Identify the sentiment and emotional tone of the provided text, categorizing it as 'positive', 'negative', or 'neutral', and provide a detailed analysis of its emotional resonance",0.9 -6,"Evaluate the sentiment of the provided text, labeling it as positive, negative, optimistic, pessimistic, or neutral while considering its tone and emotional connotation.",0.9 -6,"Determine the emotional tone of the input text and label it as either 'positive' or 'negative', effectively identifying its sentiment.",0.9 -6,"Determine the emotional tone of the given text by categorizing it into one of three sentiment categories: positive, negative, or neutral.",0.9 -7,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -7,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -7,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -7,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -7,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -7,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -7,"Identify the sentiment and emotional tone of the provided text, categorizing it as 'positive', 'negative', or 'neutral', and provide a detailed analysis of its emotional resonance",0.9 -7,"Evaluate the sentiment of the provided text, labeling it as positive, negative, optimistic, pessimistic, or neutral while considering its tone and emotional connotation.",0.9 -7,"Determine the emotional tone of the input text and label it as either 'positive' or 'negative', effectively identifying its sentiment.",0.9 -7,"Determine the emotional tone of the given text by categorizing it into one of three sentiment categories: positive, negative, or neutral.",0.9 -8,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -8,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -8,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -8,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -8,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -8,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -8,"Identify the sentiment and emotional tone of the provided text, categorizing it as 'positive', 'negative', or 'neutral', and provide a detailed analysis of its emotional resonance",0.9 -8,"Evaluate the sentiment of the provided text, labeling it as positive, negative, optimistic, pessimistic, or neutral while considering its tone and emotional connotation.",0.9 -8,"Determine the emotional tone of the input text and label it as either 'positive' or 'negative', effectively identifying its sentiment.",0.9 -8,"Determine the emotional tone of the given text by categorizing it into one of three sentiment categories: positive, negative, or neutral.",0.9 -9,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -9,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -9,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -9,"Evaluate the emotional tone of the provided text, classifying it as either 'positive' or 'negative', while distinguishing between authentic sentiments and perceived emotions.",0.95 -9,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -9,"Determine the text's sentiment by categorizing it into either positive, negative, or neutral tones and identify the most dominant emotional tone.",0.95 -9,"Determine the sentiment of the given text by identifying its emotional undertone, categorizing it as either positive or negative, or neutral.",0.95 -9,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -9,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -9,"Identify the sentiment and emotional tone of the provided text, categorizing it as 'positive', 'negative', or 'neutral', and provide a detailed analysis of its emotional resonance",0.9 -10,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -10,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -10,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -10,"Evaluate the emotional tone of the provided text, classifying it as either 'positive' or 'negative', while distinguishing between authentic sentiments and perceived emotions.",0.95 -10,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -10,"Determine the text's sentiment by categorizing it into either positive, negative, or neutral tones and identify the most dominant emotional tone.",0.95 -10,"Determine the sentiment of the given text by identifying its emotional undertone, categorizing it as either positive or negative, or neutral.",0.95 -10,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -10,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -10,"Identify the sentiment and emotional tone of the provided text, categorizing it as 'positive', 'negative', or 'neutral', and provide a detailed analysis of its emotional resonance",0.9 -11,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -11,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -11,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -11,"Evaluate the emotional tone of the provided text, classifying it as either 'positive' or 'negative', while distinguishing between authentic sentiments and perceived emotions.",0.95 -11,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -11,"Determine the text's sentiment by categorizing it into either positive, negative, or neutral tones and identify the most dominant emotional tone.",0.95 -11,"Determine the sentiment of the given text by identifying its emotional undertone, categorizing it as either positive or negative, or neutral.",0.95 -11,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -11,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -11,"Investigate the primary emotional undertone of a text, classifying it as optimistic, pessimistic, or neutral, and provide a comprehensive breakdown of the sentiment.",0.9 -12,"Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.",1.0 -12,"Determine the sentiment of the given text by categorizing it as positive, negative, optimistic, pessimistic, or neutral",1.0 -12,"Identify the sentiment of the given text as 'positive' or 'negative', considering its emotional undertone",0.95 -12,"Evaluate the emotional tone of the provided text, classifying it as either 'positive' or 'negative', while distinguishing between authentic sentiments and perceived emotions.",0.95 -12,"Evaluate the emotional tone of a sentence, labeling it as optimistic, negative, or neutral, and provide a detailed analysis of the sentiment.",0.95 -12,"Determine the text's sentiment by categorizing it into either positive, negative, or neutral tones and identify the most dominant emotional tone.",0.95 -12,"Determine the sentiment of the given text by identifying its emotional undertone, categorizing it as either positive or negative, or neutral.",0.95 -12,Classify the input text's emotional tone as either 'positive' or 'negative' through sentiment analysis.,0.95 -12,"Assess the given statement for its sentiment, classifying it as 'positive', 'negative', or 'neutral', and identify the emotional tone it conveys, distinguishing between genuine opinions and perceived sentiments.",0.95 -12,"Investigate the primary emotional undertone of a text, classifying it as optimistic, pessimistic, or neutral, and provide a comprehensive breakdown of the sentiment.",0.9 diff --git a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 9d8b850..0000000 --- a/logs/experiment/cr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -1,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -1,"<Sentiment Analysis> Determine the emotional tone of the provided text, categorizing it as either positive or negative.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.85 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.8 -1,"Determine the sentiment polarity of the given text, automatically classifying it as either positive or negative sentiment.",0.8 -1,Determine the sentiment (positive or negative) of the provided statement.,0.8 -2,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -2,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -2,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -2,"Classify the given statement as having a sentiment of either 'negative' or 'positive', and return the assigned label.",0.9 -2,"Classify the given statement as having a positive or negative sentiment and provide a corresponding label from ['negative', 'positive'].",0.9 -2,"Classify the given sentence or tweet's sentiment as 'negative' or 'positive', returning the sentiment label.",0.9 -2,"<Sentiment Analysis> Determine the emotional tone of the provided text, categorizing it as either positive or negative.",0.9 -3,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -3,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -3,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -3,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -3,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -3,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -3,"Determine the sentiment of the given statement, categorizing it as either 'positive' or 'negative' and returning the assigned classification.",0.9 -4,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -4,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -4,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -4,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -4,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -4,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -4,"Determine the sentiment of the given statement, categorizing it as either 'positive' or 'negative' and returning the assigned classification.",0.9 -5,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -5,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -5,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -5,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -5,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -5,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -5,"Determine the sentiment of the given statement, categorizing it as either 'positive' or 'negative' and returning the assigned classification.",0.9 -6,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -6,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -6,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -6,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -6,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -6,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -6,"Examine the given text to determine its sentiment, distinguishing between expressions of positivity and negativity, and return a sentiment label.",0.9 -7,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -7,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -7,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -7,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -7,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -7,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -7,"Examine the given text to determine its sentiment, distinguishing between expressions of positivity and negativity, and return a sentiment label.",0.9 -8,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -8,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -8,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -8,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -8,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -8,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -8,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -8,"Examine the given text to determine its sentiment, distinguishing between expressions of positivity and negativity, and return a sentiment label.",0.9 -9,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -9,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -9,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -9,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -9,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -9,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -9,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -9,"Examine the given text to determine its sentiment, distinguishing between expressions of positivity and negativity, and return a sentiment label.",0.9 -10,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -10,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -10,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -10,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -10,"Identify the sentiment of the given text, labeling it as 'positive' or 'negative', and determine whether it expresses a positive or negative opinion.",0.9 -10,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -10,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -10,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -10,"Given a sentence or tweet, classify its sentiment as 'negative' or 'positive'.",0.9 -11,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -11,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -11,"Determine the sentiment of a provided sentence or tweet, categorizing it as either 'positive' or 'negative'.",0.95 -11,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -11,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -11,"Identify the sentiment of the given text, labeling it as 'positive' or 'negative', and determine whether it expresses a positive or negative opinion.",0.9 -11,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -11,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -11,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 -12,Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.,0.95 -12,"Determine the sentiment of the provided tweet or sentence, categorizing it as either 'positive' or 'negative'.",0.95 -12,"Determine the sentiment of a provided sentence or tweet, categorizing it as either 'positive' or 'negative'.",0.95 -12,Categorize a statement or tweet as conveying either a positive or negative opinion or sentiment.,0.95 -12,"Assess the emotional tone of the provided text, categorizing it as either 'negative' or 'positive' and specifying the sentiment label.",0.95 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -12,"Identify the sentiment of the given text, labeling it as 'positive' or 'negative', and determine whether it expresses a positive or negative opinion.",0.9 -12,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -12,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -12,"Given a sentence, classify the sentiment as either 'positive' or 'negative'.",0.9 diff --git a/logs/experiment/mr_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv b/logs/experiment/mr_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv deleted file mode 100644 index ce98443..0000000 --- a/logs/experiment/mr_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,Classify a sentence as expressing optimism or pessimism.,0.8 -1,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -1,"Given text, classify whether it is positive or negative.",0.75 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.6 -1,"As you evaluate the social media post, determine its sentiment with a sentiment that is either positive or negative, considering the text provided. ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -5,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -5,"As a sentiment classifier,",0.75 -5,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -5,"The objective is to categorize the statement of a sentence, determining if it expresses optimism or pessimism, or conveys a positive or negative tone, while analyzing the utterance.",0.75 -5,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -5,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.5 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text - -Different parts: - -""analyze messages on social media"" vs ""Given the sentence"" -""optimism or pessimism"" vs ""a sentiment label from ['negative', 'positive']"" - -2. Randomly mutate the different parts: -""analyze messages on social media"" -> ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -6,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -6,"As a sentiment classifier,",0.75 -6,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -6,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -6,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -6,"As a sentiment classifier, analyze the tone disclosed in the given movie review and categorize",0.6 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text - -Different parts: - -""analyze messages on social media"" vs ""Given the sentence"" -""optimism or pessimism"" vs ""a sentiment label from ['negative', 'positive']"" - -2. Randomly mutate the different parts: -""analyze messages on social media"" -> ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -7,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -7,"As a sentiment classifier,",0.75 -7,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -7,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -7,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -7,"As a sentiment classifier, analyze the tone disclosed in the given movie review and categorize",0.6 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text - -Different parts: - -""analyze messages on social media"" vs ""Given the sentence"" -""optimism or pessimism"" vs ""a sentiment label from ['negative', 'positive']"" - -2. Randomly mutate the different parts: -""analyze messages on social media"" -> ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -8,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -8,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -8,"As a sentiment classifier,",0.75 -8,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -8,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -8,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -8,"As a sentiment classifier, review a passage from a social media post and categorize it according to its polarized emotional tone, ranging from optimistic to pessimistic.",0.7 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text - -Different parts: - -""analyze messages on social media"" vs ""Given the sentence"" -""optimism or pessimism"" vs ""a sentiment label from ['negative', 'positive']"" - -2. Randomly mutate the different parts: -""analyze messages on social media"" -> ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -9,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -9,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -9,"As a sentiment classifier,",0.75 -9,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -9,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -9,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -9,"As a sentiment classifier, review a passage from a social media post and categorize it according to its polarized emotional tone, ranging from optimistic to pessimistic.",0.7 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text - -Different parts: - -""analyze messages on social media"" vs ""Given the sentence"" -""optimism or pessimism"" vs ""a sentiment label from ['negative', 'positive']"" - -2. Randomly mutate the different parts: -""analyze messages on social media"" -> ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -10,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -10,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -10,"As a sentiment classifier,",0.75 -10,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -10,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -10,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -10,"As a sentiment classifier, review a passage from a social media post and categorize it according to its polarized emotional tone, ranging from optimistic to pessimistic.",0.7 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text - -Different parts: - -""analyze messages on social media"" vs ""Given the sentence"" -""optimism or pessimism"" vs ""a sentiment label from ['negative', 'positive']"" - -2. Randomly mutate the different parts: -""analyze messages on social media"" -> ""examine the reviews"" -""optimism or pessimism"" -> ""positive or negative sentiment"" - -""as a sentiment analyst"" -> ""in this sentiment evaluation task"" - -Let me know if you'd like me to continue or if you have any questions!",0.6 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -11,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -11,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -11,"As a sentiment classifier,",0.75 -11,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -11,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -11,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -11,"As a sentiment classifier, review a passage from a social media post and categorize it according to its polarized emotional tone, ranging from optimistic to pessimistic.",0.7 -11,"In this sentiment evaluation task, examine the reviews and categorize them as having positive or negative sentiment from ['negative', 'positive']. Return label only without any other text.",0.95 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -12,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -12,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -12,"As a sentiment classifier,",0.75 -12,Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.,0.95 -12,"Your task is to analyze the opinion of a sentence, categorizing it into one of the following sentiment categories: positive, neutral, negative, by considering the tone and expression of the utterance.",0.85 -12,"As a sentiment analyst, analyze messages on social media and categorize the text into either expressing optimism or pessimism.",0.9 -12,"As a sentiment classifier, review a passage from a social media post and categorize it according to its polarized emotional tone, ranging from optimistic to pessimistic.",0.7 -12,"In this sentiment evaluation task, examine the reviews and categorize them as having positive or negative sentiment from ['negative', 'positive']. Return label only without any other text.",0.95 diff --git a/logs/experiment/mr_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/mr_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 1b60601..0000000 --- a/logs/experiment/mr_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -1,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -1,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -1,"Given text, classify whether it is positive or negative.",0.75 -1,"The task is to classify the emotional tone of a piece of writing and analyze a writing to determine its sentiment, positive or negative, by considering the content and relevance of the provided text",0.85 -2,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -2,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -2,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -2,Classify the given text as expressing positive or negative sentiment,0.9 -2,"The task is to classify the emotional tone of a piece of writing and analyze a writing to determine its sentiment, positive or negative, by considering the content and relevance of the provided text",0.85 -3,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -3,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -3,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -3,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -3,Classify the given text as expressing positive or negative sentiment,0.9 -3,"The task is to classify the emotional tone of a piece of writing and analyze a writing to determine its sentiment, positive or negative, by considering the content and relevance of the provided text",0.85 -4,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -4,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -4,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -4,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -4,Classify the given text as expressing positive or negative sentiment,0.9 -4,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -5,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -5,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -5,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -5,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -5,Classify the given text as expressing positive or negative sentiment,0.9 -5,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -6,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -6,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -6,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -6,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -6,Classify the given text as expressing positive or negative sentiment,0.9 -6,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -7,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -7,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -7,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.8 -7,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.75 -7,Classify the given text as expressing positive or negative sentiment,0.9 -7,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -8,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -8,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -8,"Analyze the emotional tone of the text and assign a sentiment label from ['negative', 'positive'] to classify the sentence",0.9 -8,"As a sentiment analyzer, read and analyze the passage and determine its sentiment as either positive or negative.",0.9 -8,"As a sentiment classifier, analyze the mentioned text with a positive or negative tone and determine its sentiment polarity, taking",0.8 -8,Classify the given text as expressing positive or negative sentiment,0.9 -8,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -9,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -9,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -9,"Analyze the emotional tone of the text and assign a sentiment label from ['negative', 'positive'] to classify the sentence",0.9 -9,"As a sentiment analyzer, read and analyze the passage and determine its sentiment as either positive or negative.",0.9 -9,"As a sentiment classifier, analyze the mentioned text with a positive or negative tone and determine its sentiment polarity, taking",0.8 -9,Classify the given text as expressing positive or negative sentiment,0.9 -9,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -10,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -10,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -10,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -10,"Analyze the emotional tone of the text and assign a sentiment label from ['negative', 'positive'] to classify the sentence",0.9 -10,"As a sentiment analyzer, read and analyze the passage and determine its sentiment as either positive or negative.",0.9 -10,"As a sentiment classifier, analyze the mentioned text with a positive or negative tone and determine its sentiment polarity, taking",0.8 -10,Classify the given text as expressing positive or negative sentiment,0.9 -10,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -11,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -11,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -11,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -11,"Analyze the emotional tone of the text and assign a sentiment label from ['negative', 'positive'] to classify the sentence",0.9 -11,"As a sentiment analyzer, read and analyze the passage and determine its sentiment as either positive or negative.",0.9 -11,"As a sentiment classifier, analyze the mentioned text with a positive or negative tone and determine its sentiment polarity, taking",0.8 -11,Classify the given text as expressing positive or negative sentiment,0.9 -11,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 -12,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -12,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.9 -12,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -12,"Analyze the emotional tone of the text and assign a sentiment label from ['negative', 'positive'] to classify the sentence",0.9 -12,"As a sentiment analyzer, read and analyze the passage and determine its sentiment as either positive or negative.",0.9 -12,"As a sentiment classifier, analyze the mentioned text with a positive or negative tone and determine its sentiment polarity, taking",0.8 -12,Classify the given text as expressing positive or negative sentiment,0.9 -12,"As a sentiment analyzer, examine written content and categorize it based on its emotional resonance, classifying it as positive or negative",0.9 diff --git a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv b/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv deleted file mode 100644 index 8db0920..0000000 --- a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -1,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -1,"Given text, classify whether it is positive or negative.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -1,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -1,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional tone.",0.9 -1,"Determine the sentiment of the provided sentence, categorizing it as either ""positive"" or ""negative"".",0.9 -2,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -2,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -2,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -2,"Given text, classify whether it is positive or negative.",0.9 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -2,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -2,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -2,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional tone.",0.9 -2,"Determine the sentiment of the provided sentence, categorizing it as either ""positive"" or ""negative"".",0.9 -3,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -3,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -3,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -3,"Given text, classify whether it is positive or negative.",0.9 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -3,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -3,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -3,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"" based on its emotional tone.",0.9 -3,"Determine the sentiment of the provided sentence, categorizing it as either ""positive"" or ""negative"".",0.9 -4,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -4,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -4,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -4,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -4,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -4,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -4,"Given text, classify whether it is positive or negative.",0.9 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -4,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -5,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -5,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -5,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -5,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -5,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -5,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -5,"Given text, classify whether it is positive or negative.",0.9 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -5,"Determine the sentiment of the provided text, categorizing it as either ""positive"" or ""negative"".",0.9 -6,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -6,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -6,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -6,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -6,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -6,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -6,"Identify the emotional tone of the given text, labelling it as having either a ""positive"" or ""negative"" sentiment.",0.9 -6,"Given text, classify whether it is positive or negative.",0.9 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -7,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -7,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -7,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -7,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -7,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -7,"Analyze the given comment and label its emotional tone as either ""positive"" or ""negative"" sentiment.",0.95 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -7,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -7,"Identify the emotional tone of the given text, labelling it as having either a ""positive"" or ""negative"" sentiment.",0.9 -7,"Given text, classify whether it is positive or negative.",0.9 -8,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -8,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -8,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -8,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -8,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -8,"Analyze the given comment and label its emotional tone as either ""positive"" or ""negative"" sentiment.",0.95 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -8,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -8,"Identify the emotional tone of the given text, labelling it as having either a ""positive"" or ""negative"" sentiment.",0.9 -8,"Given text, classify whether it is positive or negative.",0.9 -9,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -9,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -9,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -9,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -9,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -9,"Analyze the given comment and label its emotional tone as either ""positive"" or ""negative"" sentiment.",0.95 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -9,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -9,"Identify the emotional tone of the given text, labelling it as having either a ""positive"" or ""negative"" sentiment.",0.9 -9,"Given text, classify whether it is positive or negative.",0.9 -10,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -10,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -10,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -10,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -10,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -10,"Determine the emotional sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -10,"Analyze the given comment and label its emotional tone as either ""positive"" or ""negative"" sentiment.",0.95 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -10,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -10,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -11,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -11,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -11,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -11,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -11,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -11,"Determine the emotional sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -11,"Analyze the given comment and label its emotional tone as either ""positive"" or ""negative"" sentiment.",0.95 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -11,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -11,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -12,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -12,Analyze the provided sentence and label its sentiment as positive or negative.,1.0 -12,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -12,"Determine the emotional tone of the provided text, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -12,"Determine the emotional tone of the given text and label it as either ""positive"" or ""negative"" accordingly.",0.95 -12,"Determine the emotional sentiment of the provided text, labeling it as either ""positive"" or ""negative"".",0.95 -12,"Analyze the given comment and label its emotional tone as either ""positive"" or ""negative"" sentiment.",0.95 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -12,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 -12,"Identify the sentiment of the provided text by categorizing it as either ""positive"" or ""negative"".",0.9 diff --git a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv b/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv deleted file mode 100644 index b39e201..0000000 --- a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,Determine whether a provided statement conveys a positive or negative attitude.,0.9 -1,"Determine the sentiment of a movie review sentence, categorizing it as either ""positive"" or ""negative"".",0.9 -1,"Determine the sentiment expressed in the movie review, categorizing it as either ""positive"" or ""negative"" towards the film.",0.9 -1,"Interpret the sentiment of a provided statement, considering its context and meaning, and categorize it as either positive or negative.",0.85 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.85 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.85 -2,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -2,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,Determine whether a provided statement conveys a positive or negative attitude.,0.9 -2,"Determine the sentiment of a movie review sentence, categorizing it as either ""positive"" or ""negative"".",0.9 -2,"Determine the sentiment expressed in the movie review, categorizing it as either ""positive"" or ""negative"" towards the film.",0.9 -2,"Interpret the sentiment of a provided statement, considering its context and meaning, and categorize it as either positive or negative.",0.85 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.85 -3,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -3,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -3,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,Determine whether a provided statement conveys a positive or negative attitude.,0.9 -3,"Determine the sentiment of a movie review sentence, categorizing it as either ""positive"" or ""negative"".",0.9 -3,"Determine the sentiment expressed in the movie review, categorizing it as either ""positive"" or ""negative"" towards the film.",0.9 -3,"Interpret the sentiment of a provided statement, considering its context and meaning, and categorize it as either positive or negative.",0.85 -4,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -4,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -4,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -4,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -4,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -4,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -4,"Analyze the given sentence to identify its sentiment, categorizing it as either ""positive"" or ""negative"" in tone.",0.95 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -4,Identify the sentiment of a given sentence or text by determining whether it conveys a positive or negative emotion.,0.9 -5,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -5,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -5,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -5,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -5,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -5,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -5,"Analyze the given sentence to identify its sentiment, categorizing it as either ""positive"" or ""negative"" in tone.",0.95 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -5,Identify the sentiment of a given sentence or text by determining whether it conveys a positive or negative emotion.,0.9 -6,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -6,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -6,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -6,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -6,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -6,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -6,"Analyze the given sentence to identify its sentiment, categorizing it as either ""positive"" or ""negative"" in tone.",0.95 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.9 -6,Identify the sentiment of a given sentence or text by determining whether it conveys a positive or negative emotion.,0.9 -7,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -7,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -7,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -7,"Determine the sentiment of the provided text and label it as either ""positive"" or ""negative"".",0.95 -7,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -7,"Determine the sentiment of the input sentence, categorizing it as either ""positive"" or ""negative"" based on the expressed attitude.",0.95 -7,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -7,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -7,"Determine the emotional tone of a provided movie review, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -7,"Analyze the given sentence to identify its sentiment, categorizing it as either ""positive"" or ""negative"" in tone.",0.95 -8,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -8,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -8,"Identify the sentiment polarity of the provided movie review, categorizing it as either ""positive"" or ""negative"" based on the expressed opinion.",0.95 -8,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -8,"Determine the sentiment of the provided text and label it as either ""positive"" or ""negative"".",0.95 -8,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -8,"Determine the sentiment of the input sentence, categorizing it as either ""positive"" or ""negative"" based on the expressed attitude.",0.95 -8,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -8,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -8,"Determine the emotional tone of a provided movie review, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -9,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -9,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -9,"Identify the sentiment polarity of the provided movie review, categorizing it as either ""positive"" or ""negative"" based on the expressed opinion.",0.95 -9,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -9,"Determine the sentiment of the provided text and label it as either ""positive"" or ""negative"".",0.95 -9,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -9,"Determine the sentiment of the input sentence, categorizing it as either ""positive"" or ""negative"" based on the expressed attitude.",0.95 -9,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -9,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -9,"Determine the emotional tone of a provided movie review, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -10,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -10,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -10,"Identify the sentiment polarity of the provided movie review, categorizing it as either ""positive"" or ""negative"" based on the expressed opinion.",0.95 -10,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -10,"Determine the sentiment of the provided text and label it as either ""positive"" or ""negative"".",0.95 -10,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -10,"Determine the sentiment of the input sentence, categorizing it as either ""positive"" or ""negative"" based on the expressed attitude.",0.95 -10,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -10,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -10,"Determine the emotional tone of a provided movie review, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -11,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -11,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -11,"Identify the sentiment polarity of the provided movie review, categorizing it as either ""positive"" or ""negative"" based on the expressed opinion.",0.95 -11,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -11,"Determine the sentiment of the provided text and label it as either ""positive"" or ""negative"".",0.95 -11,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -11,"Determine the sentiment of the input sentence, categorizing it as either ""positive"" or ""negative"" based on the expressed attitude.",0.95 -11,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -11,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -11,"Determine the emotional tone of a provided movie review, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 -12,"Classify the sentiment of the provided sentence or review as either ""positive"" or ""negative"", indicating the attitude towards the subject.",1.0 -12,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -12,"Identify the sentiment polarity of the provided movie review, categorizing it as either ""positive"" or ""negative"" based on the expressed opinion.",0.95 -12,Determine whether the given movie review expresses a positive or negative sentiment.,0.95 -12,"Determine the sentiment of the provided text and label it as either ""positive"" or ""negative"".",0.95 -12,"Determine the sentiment of the provided statement, taking into account its context and nuances, and label it as either 'positive' or 'negative'.",0.95 -12,"Determine the sentiment of the input sentence, categorizing it as either ""positive"" or ""negative"" based on the expressed attitude.",0.95 -12,"Determine the sentiment of the given text, labeling it as either ""positive"" or ""negative"" to reflect the underlying attitude.",0.95 -12,"Determine the emotional tone of the provided sentence and label it as either ""positive"" or ""negative"" sentiment.",0.95 -12,"Determine the emotional tone of a provided movie review, categorizing it as either ""positive"" or ""negative"" sentiment.",0.95 diff --git a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv b/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv deleted file mode 100644 index 74f450e..0000000 --- a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -1,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -1,Classify the sentiment of a provided text as either positive or negative.,0.9 -1,"Determine the sentiment of the provided text by understanding its context and meaning, and then label it as either positive or negative.",0.85 -1,"Determine the sentiment of a provided sentence or comment, categorizing it as either ""positive"" or ""negative"".",0.85 -1,Analyze the provided text to identify its sentiment as either positive or negative.,0.8 -1,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.75 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -2,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -2,"Classify the sentiment of a provided text, tweet, or comment as either positive or negative, based on its content.",0.9 -2,Classify the sentiment of a provided text as either positive or negative.,0.9 -2,"Assign a sentiment label (""positive"" or ""negative"") to the given sentence or comment, indicating its emotional tone.",0.9 -2,Analyze the provided sentence and label its sentiment as either positive or negative.,0.9 -2,"Examine the provided text carefully to identify its sentiment, classifying it as either positive or negative based on its underlying tone and meaning.",0.85 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -3,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -3,"Determine the sentiment of a given text, categorizing it as either positive or negative.",0.9 -3,"Determine the emotional tone of the given text, categorizing it as either positive or negative based on a thorough analysis of its content and underlying meaning.",0.9 -3,"Classify the sentiment of a provided text, tweet, or comment as either positive or negative, based on its content.",0.9 -3,Classify the sentiment of a provided text as either positive or negative.,0.9 -3,"Assign a sentiment label (""positive"" or ""negative"") to the given sentence or comment, indicating its emotional tone.",0.9 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -4,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -4,"Determine the sentiment of a given text, categorizing it as either positive or negative.",0.9 -4,"Determine the emotional tone of the given text, categorizing it as either positive or negative based on a thorough analysis of its content and underlying meaning.",0.9 -4,"Classify the sentiment of a provided text, tweet, or comment as either positive or negative, based on its content.",0.9 -4,Classify the sentiment of a provided text as either positive or negative.,0.9 -4,"Assign a sentiment label (""positive"" or ""negative"") to the given sentence or comment, indicating its emotional tone.",0.9 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -5,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -5,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -5,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -5,"Determine the sentiment of a given text, categorizing it as either positive or negative.",0.9 -5,"Determine the emotional tone of the provided sentence or comment, labeling it as either ""positive"" or ""negative"".",0.9 -5,"Determine the emotional tone of the provided sentence or comment and label it as either ""positive"" or ""negative"" sentiment.",0.9 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -6,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -6,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -6,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -6,Analyze the tone and language of the provided text to determine whether it conveys a positive or negative sentiment.,0.95 -6,"Analyze the provided text to identify its sentiment as positive or negative, taking into account the subtleties of language and tone.",0.95 -6,"Determine the sentiment of a given text, categorizing it as either positive or negative.",0.9 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -7,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -7,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -7,"Determine the sentiment of the provided statement, either positive or negative, by carefully examining the language nuances and tone.",0.95 -7,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -7,Analyze the tone and language of the provided text to determine whether it conveys a positive or negative sentiment.,0.95 -7,"Analyze the provided text to identify its sentiment as positive or negative, taking into account the subtleties of language and tone.",0.95 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -8,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -8,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -8,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -8,"Determine the sentiment of the provided statement, either positive or negative, by carefully examining the language nuances and tone.",0.95 -8,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -8,Analyze the tone and language of the provided text to determine whether it conveys a positive or negative sentiment.,0.95 -8,"Analyze the provided text to identify its sentiment as positive or negative, taking into account the subtleties of language and tone.",0.95 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -9,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -9,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -9,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -9,"Determine the sentiment of the provided statement, either positive or negative, by carefully examining the language nuances and tone.",0.95 -9,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -9,Assess the tone and language nuances of the given text to categorize its sentiment as positive or negative.,0.95 -9,Analyze the tone and language of the provided text to determine whether it conveys a positive or negative sentiment.,0.95 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -10,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -10,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -10,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -10,"Determine the sentiment of the provided statement, either positive or negative, by carefully examining the language nuances and tone.",0.95 -10,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -10,Classify the sentiment of the provided text as 'positive' or 'negative' based on its content.,0.95 -10,Assess the tone and language nuances of the given text to categorize its sentiment as positive or negative.,0.95 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -11,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -11,"Analyze the given text to identify its sentiment as positive or negative, paying attention to the subtleties of language and tone.",1.0 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -11,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -11,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -11,"Determine the sentiment of the provided text as positive or negative, taking into account its linguistic tone and subtleties.",0.95 -11,"Determine the sentiment of the provided statement, either positive or negative, by carefully examining the language nuances and tone.",0.95 -11,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",1.0 -12,"Given a statement, classify it as expressing a positive or negative opinion.",1.0 -12,"Analyze the given text to identify its sentiment as positive or negative, paying attention to the subtleties of language and tone.",1.0 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -12,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -12,"Evaluate the provided text and classify its sentiment as positive or negative, taking into account the nuances of its language and tone.",0.95 -12,"Determine the sentiment of the provided text as positive or negative, taking into account its linguistic tone and subtleties.",0.95 -12,"Determine the sentiment of the provided statement, either positive or negative, by carefully examining the language nuances and tone.",0.95 -12,"Determine the sentiment of a provided statement, tweet, or comment as either positive or negative by analyzing its content.",0.95 diff --git a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 3b16a6d..0000000 --- a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -1,Classify the input sentence as 'negative' or 'positive',0.95 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,Classify the given text as either positive or negative sentiment.,0.85 -1,"Analyzer, categorize the given text or comment as either optimistic or pessimistic.",0.8 -1,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -1,"Given text, classify whether it is positive or negative.",0.75 -1,"Determine the sentiment rating of a given text snippet, ranging from ""very positive"" to ""very negative"".",0.75 -2,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -2,Classify the input sentence as 'negative' or 'positive',0.95 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -2,"Determine the sentiment of a given input, whether it's a single sentence or a larger text, categorizing it as either positive or negative.",0.9 -2,"Determine the emotional tone of the text by categorizing it as very positive, somewhat positive, neutral, somewhat negative, or very negative. Evaluate the sentiment and provide a rating.",0.9 -2,"Determine the sentiment of an input sentence, classifying it as positive, negative, or neutral.",0.85 -2,Classify the sentence's sentiment as 'negative' or 'positive'.,0.85 -3,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -3,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -3,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -3,Classify the input sentence as 'negative' or 'positive',0.95 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -3,"Determine whether the given text is emotionally positive or negative, categorizing it as either 'positive' or 'negative'",0.9 -3,"Determine the sentiment of the given sentence, categorizing it as either 'negative' or 'positive'",0.9 -4,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -4,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -4,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -4,Classify the input sentence as 'negative' or 'positive',0.95 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -4,"Determine whether the given text is emotionally positive or negative, categorizing it as either 'positive' or 'negative'",0.9 -4,"Determine the sentiment of the given sentence, categorizing it as either 'negative' or 'positive'",0.9 -5,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -5,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -5,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -5,Classify the input sentence as 'negative' or 'positive',0.95 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -5,"Determine whether the given text is emotionally positive or negative, categorizing it as either 'positive' or 'negative'",0.9 -5,"Determine the text's emotional tone, categorizing it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and further nuance the classification as either 'positive' or 'negative'",0.9 -6,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -6,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -6,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -6,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -6,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -6,Classify the input sentence as 'negative' or 'positive',0.95 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.9 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -7,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -7,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -7,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -7,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -7,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -7,"Determine the emotional tone of the given text, assessing its sentiment with a specified degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and categorize it as either ""positive"" or ""negative"" to provide a precise sentiment analysis.",0.95 -7,Classify the input sentence as 'negative' or 'positive',0.95 -7,Classify the given text as 'positive' or 'negative' by performing sentiment analysis and returning the resulting label.,0.95 -7,Classify the given sentence into positive or negative sentiment and return the corresponding label ('negative' or 'positive').,0.95 -8,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -8,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -8,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -8,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -8,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -8,"Determine the emotional tone of the given text, assessing its sentiment with a specified degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and categorize it as either ""positive"" or ""negative"" to provide a precise sentiment analysis.",0.95 -8,Classify the input sentence as 'negative' or 'positive',0.95 -8,Classify the given text as 'positive' or 'negative' by performing sentiment analysis and returning the resulting label.,0.95 -8,Classify the given sentence into positive or negative sentiment and return the corresponding label ('negative' or 'positive').,0.95 -9,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -9,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -9,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -9,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -9,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -9,"Determine the emotional tone of the given text, assessing its sentiment with a specified degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and categorize it as either ""positive"" or ""negative"" to provide a precise sentiment analysis.",0.95 -9,Classify the input sentence as 'negative' or 'positive',0.95 -9,Classify the given text as 'positive' or 'negative' by performing sentiment analysis and returning the resulting label.,0.95 -9,Classify the given sentence into positive or negative sentiment and return the corresponding label ('negative' or 'positive').,0.95 -10,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -10,"Analyze the sentiment of the given text, distinguishing between very positive, somewhat positive, neutral, somewhat negative, and very negative emotional tones, and return the corresponding 'positive' or 'negative' label for a precise emotional assessment.",1.0 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -10,"Identify the emotional tone and sentiment of the input text, with a detailed assessment of its positivity (very positive, somewhat positive, neutral, somewhat negative, or very negative) and classification as either ""positive"" or ""negative"".",0.95 -10,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -10,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -10,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -10,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -10,"Determine the emotional tone of the given text, assessing its sentiment with a specified degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and categorize it as either ""positive"" or ""negative"" to provide a precise sentiment analysis.",0.95 -10,"Determine the emotional tone of the given text and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, while indicating whether the overall sentiment is overall positive or negative.",0.95 -11,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -11,"Analyze the sentiment of the given text, distinguishing between very positive, somewhat positive, neutral, somewhat negative, and very negative emotional tones, and return the corresponding 'positive' or 'negative' label for a precise emotional assessment.",1.0 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -11,"Identify the emotional tone and sentiment of the input text, with a detailed assessment of its positivity (very positive, somewhat positive, neutral, somewhat negative, or very negative) and classification as either ""positive"" or ""negative"".",0.95 -11,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -11,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -11,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -11,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -11,"Determine the emotional tone of the given text, assessing its sentiment with a specified degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and categorize it as either ""positive"" or ""negative"" to provide a precise sentiment analysis.",0.95 -11,"Determine the emotional tone of the given text and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, while indicating whether the overall sentiment is overall positive or negative.",0.95 -12,Assess the given text and categorize it as either a positive or negative sentiment.,1.0 -12,"Analyze the sentiment of the given text, distinguishing between very positive, somewhat positive, neutral, somewhat negative, and very negative emotional tones, and return the corresponding 'positive' or 'negative' label for a precise emotional assessment.",1.0 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -12,"Identify the emotional tone and sentiment of the input text, with a detailed assessment of its positivity (very positive, somewhat positive, neutral, somewhat negative, or very negative) and classification as either ""positive"" or ""negative"".",0.95 -12,"Evaluate the sentiment of the text and categorize it as indicating a very positive, somewhat positive, neutral, somewhat negative, or very negative tone, and further classify it as either ""positive"" or ""negative"" for a nuanced understanding.",0.95 -12,"Determine the sentiment of the provided text, whether short or lengthy, categorizing it as positive, negative, or neutral in tone.",0.95 -12,"Determine the input sentence's emotional tone and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, and nuance the classification as overall positive or negative.",0.95 -12,"Determine the emotional tone of the input text, specifying its degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and whether it's overall positive or negative.",0.95 -12,"Determine the emotional tone of the given text, assessing its sentiment with a specified degree (very positive, somewhat positive, neutral, somewhat negative, or very negative) and categorize it as either ""positive"" or ""negative"" to provide a precise sentiment analysis.",0.95 -12,"Determine the emotional tone of the given text and classify it as very positive, somewhat positive, neutral, somewhat negative, or very negative, while indicating whether the overall sentiment is overall positive or negative.",0.95 diff --git a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 1b58f5d..0000000 --- a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -1,"Classify the sentiment towards a movie based on a provided review, returning either 'positive' or 'negative'",0.85 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.8 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -1,Classify the input text as having a positive or negative tone,0.8 -1,"Analyze the input text to determine its emotional tone, categorizing it as ""positive"", ""negative"", or ""neutral"" sentiment.",0.8 -1,Assign a sentiment classification (positive or negative) and a tone classification (optimistic or pessimistic) to the provided statement.,0.75 -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.7 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -2,Identify the emotional tone of a given statement as either positive or negative sentiment,0.85 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -2,"Classify the sentiment towards a movie based on a provided review, returning either 'positive' or 'negative'",0.85 -2,"Identify the sentiment orientation (positive or negative) of the review, analyzing the given statement's opinion towards the movie.",0.8 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.8 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.8 -2,Classify the input text as having a positive or negative tone,0.8 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -3,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -3,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -3,"Assess the emotional tone of user-generated content (e.g. comments, tweets) and categorize it as having a positive or negative sentiment.",0.9 -3,Sentiment Analysis: Classify the tone of a review or statement as positive or negative.,0.85 -3,"Rank the provided text as having a positive, negative, or neutral emotional tone.",0.85 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -3,Identify the emotional tone of a given statement as either positive or negative sentiment,0.85 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.85 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -4,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -4,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -4,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -4,"Assess the emotional tone of user-generated content (e.g. comments, tweets) and categorize it as having a positive or negative sentiment.",0.9 -4,Sentiment Analysis: Classify the tone of a review or statement as positive or negative.,0.85 -4,"Rank the provided text as having a positive, negative, or neutral emotional tone.",0.85 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -4,Identify the emotional tone of a given statement as either positive or negative sentiment,0.85 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -5,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -5,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -5,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -5,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -5,"Assess the emotional tone of user-generated content (e.g. comments, tweets) and categorize it as having a positive or negative sentiment.",0.9 -5,Sentiment Analysis: Classify the tone of a review or statement as positive or negative.,0.85 -5,"Rank the provided text as having a positive, negative, or neutral emotional tone.",0.85 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.85 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -6,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -6,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -6,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -6,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -6,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -6,"Assess the emotional tone of user-generated content (e.g. comments, tweets) and categorize it as having a positive or negative sentiment.",0.9 -6,"Analyzing the emotional tone, determine if the text exudes a positive or negative sentiment",0.9 -6,Sentiment Analysis: Classify the tone of a review or statement as positive or negative.,0.85 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -7,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -7,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -7,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -7,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -7,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -7,"Classify the emotional tone of the review, categorizing it as either 'positive' or 'negative' sentiment, and provide a nuanced assessment of the reviewer's feelings.",0.9 -7,"Assess the emotional tone of user-generated content (e.g. comments, tweets) and categorize it as having a positive or negative sentiment.",0.9 -7,"Analyzing the emotional tone, determine if the text exudes a positive or negative sentiment",0.9 -8,"Determine the sentiment of the input, classifying it as positive or negative emotional tone.",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -8,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -8,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -8,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -8,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -8,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -8,"Classify the emotional tone of the review, categorizing it as either 'positive' or 'negative' sentiment, and provide a nuanced assessment of the reviewer's feelings.",0.9 -8,"Assess the tone and sentiment of the provided text, identifying whether it exhibits a positive, negative, or neutral emotional bias.",0.9 -9,"Determine the sentiment of the input, classifying it as positive or negative emotional tone.",1.0 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -9,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -9,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -9,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -9,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -9,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -9,"Classify the emotional tone of the review, categorizing it as either 'positive' or 'negative' sentiment, and provide a nuanced assessment of the reviewer's feelings.",0.9 -9,"Assess the tone and sentiment of the provided text, identifying whether it exhibits a positive, negative, or neutral emotional bias.",0.9 -10,"Determine the sentiment of the input, classifying it as positive or negative emotional tone.",1.0 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -10,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -10,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -10,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -10,"Determine the sentiment of the provided text, classifying it as either 'positive' or 'negative' in tone.",0.9 -10,"Determine the emotional tone of the input text, categorizing it as positive, negative, or neutral sentiment.",0.9 -10,Classify the input sentence as 'positive' or 'negative' sentiment,0.9 -10,"Classify the emotional tone of the review, categorizing it as either 'positive' or 'negative' sentiment, and provide a nuanced assessment of the reviewer's feelings.",0.9 -11,"Determine the sentiment of the input, classifying it as positive or negative emotional tone.",1.0 -11,"Ascertain the underlying emotional tone of the input text, categorizing it as either unambiguously positive, unambiguously negative, neutral, or a nuanced blend of both sentiment options.",1.0 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -11,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -11,"Assess the emotional tone of the input, categorizing it as either positive or negative sentiment, and provide a detailed explanation of the reviewer's emotional state.",0.95 -11,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -11,"Accurately evaluate the emotional sentiment of the input text, categorizing it as either strongly positive or negative tone.",0.95 -11,"Evaluate the sentiment or tone of the given text, determining if it has a positive or negative bias.",0.9 -11,"Determine the sentiment of the provided text, classifying it as either 'positive' or 'negative' in tone.",0.9 -12,"Determine the sentiment of the input, classifying it as positive or negative emotional tone.",1.0 -12,"Ascertain the underlying emotional tone of the input text, categorizing it as either unambiguously positive, unambiguously negative, neutral, or a nuanced blend of both sentiment options.",1.0 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Determine the sentiment of the provided review, categorizing it as either 'positive' or 'negative' in tone.",0.95 -12,"Determine and classify the emotional tone of the given review, categorizing it as 'positive', 'negative', or 'neutral' sentiment.",0.95 -12,"Assess the sentiment of the provided review, distinguishing between strongly positive, strongly negative, and neutral emotional tones.",0.95 -12,"Assess the emotional tone of the input, categorizing it as either positive or negative sentiment, and provide a detailed explanation of the reviewer's emotional state.",0.95 -12,"Analyze the emotional tone of the given input, categorizing it as either positive or negative sentiment.",0.95 -12,"Accurately evaluate the emotional sentiment of the input text, categorizing it as either strongly positive or negative tone.",0.95 -12,"Identify the dominant sentiment in the given text, classifying it as strongly positive or strongly negative tone.",0.9 diff --git a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index b7e11e6..0000000 --- a/logs/experiment/mr_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -1,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.85 -1,"Given text, classify whether it is positive or negative.",0.85 -1,"Determine the sentiment of the provided text, encompassing tweets, by categorizing it as either positive or negative in tone.",0.85 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.8 -1,"Classify the provided text as reflecting a positive or negative sentiment, ensuring accurate identification of its emotional tone.",0.8 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -2,"Classify a given text, potentially including tweets, by sentiment, returning either 'negative' or 'positive' as the outcome.",0.9 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -2,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.85 -2,"Given text, classify whether it is positive or negative.",0.85 -2,"Determine the sentiment of the provided text, encompassing tweets, by categorizing it as either positive or negative in tone.",0.85 -2,"Determine the sentiment of the provided text, categorizing it as expressing a positive or negative opinion, while accurately detecting its emotional tone.",0.85 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -3,"Classify a given text, potentially including tweets, by sentiment, returning either 'negative' or 'positive' as the outcome.",0.9 -3,"Categorize the given text, including tweets and comments, as either 'positive' or 'negative' sentiment.",0.9 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -3,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.85 -3,"Identify the sentiment of the provided text, ranging from tweets to other written expressions, and categorize it as either positive or negative tone.",0.85 -3,"Given text, classify whether it is positive or negative.",0.85 -3,"Determine the sentiment of the provided text, encompassing tweets, by categorizing it as either positive or negative in tone.",0.85 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -4,"Determine the emotional tone of the provided text, categorizing it as either 'positive' or 'negative' sentiment.",0.9 -4,"Classify a given text, potentially including tweets, by sentiment, returning either 'negative' or 'positive' as the outcome.",0.9 -4,"Categorize the given text, including tweets and comments, as either 'positive' or 'negative' sentiment.",0.9 -4,Assign a sentiment label ('negative' or 'positive') to the given sentence.,0.9 -4,"Analyze a range of text inputs, from short sentences to social media posts, and categorize them as expressing either positive or negative sentiment.",0.9 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.85 -4,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.85 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -5,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -5,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -5,"Determine the emotional tone of the provided text, categorizing it as either 'positive' or 'negative' sentiment.",0.9 -5,Classify the given sentence as either positive or negative sentiment and return the label.,0.9 -5,"Classify a given text, potentially including tweets, by sentiment, returning either 'negative' or 'positive' as the outcome.",0.9 -5,"Categorize the given text, including tweets and comments, as either 'positive' or 'negative' sentiment.",0.9 -5,Assign a sentiment label ('negative' or 'positive') to the given sentence.,0.9 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -6,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -6,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -6,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -6,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -6,"Determine the emotional tone of the provided text, categorizing it as either 'positive' or 'negative' sentiment.",0.9 -6,Classify the given sentence as either positive or negative sentiment and return the label.,0.9 -6,"Classify a given text, potentially including tweets, by sentiment, returning either 'negative' or 'positive' as the outcome.",0.9 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -7,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -7,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -7,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -7,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -7,"Determine the sentiment of the given text, categorizing it as 'positive', 'negative', or return the corresponding sentiment label ['positive', 'negative'].",0.9 -7,"Determine the emotional tone of the provided text, categorizing it as either 'positive' or 'negative' sentiment.",0.9 -7,Classify the given sentence as either positive or negative sentiment and return the label.,0.9 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -8,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -8,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -8,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -8,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -8,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -8,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -8,"Identify the emotional sentiment expressed in the provided text, determining whether it conveys a positive or negative opinion.",0.9 -8,"Determine the sentiment of the given text, categorizing it as 'positive', 'negative', or return the corresponding sentiment label ['positive', 'negative'].",0.9 -8,"Determine the emotional tone of the provided text, identifying whether it expresses a positive or negative sentiment.",0.9 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -9,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -9,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -9,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -9,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -9,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -9,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -9,"Identify the emotional sentiment expressed in the provided text, determining whether it conveys a positive or negative opinion.",0.9 -9,"Evaluate the emotional tone of the written text, determining whether its sentiment is predominantly positive or negative.",0.9 -9,"Determine the sentiment of the given text, categorizing it as 'positive', 'negative', or return the corresponding sentiment label ['positive', 'negative'].",0.9 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -10,"Identify the emotional inclination of the provided text, labeling it as either 'positive' or 'negative' sentiment, regardless of the written style.",0.95 -10,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -10,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -10,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -10,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -10,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -10,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -10,"Identify the emotional sentiment expressed in the provided text, determining whether it conveys a positive or negative opinion.",0.9 -10,"Evaluate the emotional tone of the written text, determining whether its sentiment is predominantly positive or negative.",0.9 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -11,"Identify the emotional inclination of the provided text, labeling it as either 'positive' or 'negative' sentiment, regardless of the written style.",0.95 -11,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -11,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -11,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -11,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -11,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -11,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -11,"Identify the emotional sentiment expressed in the provided text, determining whether it conveys a positive or negative opinion.",0.9 -11,"Evaluate the emotional tone of the written text, determining whether its sentiment is predominantly positive or negative.",0.9 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text",0.95 -12,"Identify the emotional inclination of the provided text, labeling it as either 'positive' or 'negative' sentiment, regardless of the written style.",0.95 -12,"Identify and categorize a diverse set of text inputs as either expressing positive or negative sentiment, encompassing various forms of written communication.",0.95 -12,"Given a statement, classify it as expressing a positive or negative opinion.",0.95 -12,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -12,Classify the input sentence as having a positive or negative sentiment and assign the appropriate label ('positive' or 'negative'),0.95 -12,"Assess the emotional inclination of the given text, assigning it as either 'positive' or 'negative' mood, while encompassing various forms of written expression.",0.95 -12,"Identify the sentiment of the given sentence, categorizing it as either positive or negative while indicating its sentiment intensity.",0.9 -12,"Identify the emotional tone of the given text, categorizing it as either positive or negative sentiment.",0.9 -12,"Identify the emotional sentiment expressed in the provided text, determining whether it conveys a positive or negative opinion.",0.9 diff --git a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv b/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv deleted file mode 100644 index c227eb9..0000000 --- a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv +++ /dev/null @@ -1,417 +0,0 @@ -step,prompt,score -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -1,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -1,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -1,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -1,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -1,"Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.",0.15 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. -Prompt 2: Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great. - -Different parts: -""In this task, you are given movie reviews"" vs ""Your goal is to analyze the movie review"" -""Based on it, classify it to one of the five classes"" vs ""assign it to one of five categories, ranging from terrible to great"" -""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" vs ""ranging from terrible to great"" - -**Step 2: Randomly mutate the different parts** - -""In this task, you are given movie reviews"" -> ""You will be evaluating a set of film reviews"" -""Based on it, classify it to one of the five classes"" -> ""to determine its sentiment",0.2 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text. -Prompt 2: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. - -Different parts: -* ""Please perform Sentiment Classification task"" vs ""Analyze the sentence"" -* ""Given the sentence, assign a sentiment label"" vs ""categorize it into one of five categories"" -* ""Return label only without any other text"" vs no equivalent phrase - -2. Randomly mutate the different parts: - -* ""Please perform Sentiment Classification task"" -> ""Conduct a sentiment analysis"" -* ""Given the sentence, assign a sentiment label"" -> ""Evaluate the review and assign a rating"" -* ""Return label only without any other text"" -> ""Provide the classification result"" -* ""Analyze the sentence"" -> ""Examine the review"" -* ""categorize it into one of five categories"" -> """,0.15 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -2,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -2,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -2,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -2,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -2,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -2,"Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.",0.15 -2,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -2,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -3,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -3,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -3,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -3,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -3,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -3,"Let's follow the instructions step-by-step: - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. -Prompt 2: Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great. - -Different parts: -""In this task, you are given movie reviews"" vs ""Categorize the movie review provided"" -""Based on it, classify it to one of the five classes"" vs ""into one of five degrees based on the sentiment"" -""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" vs ""terrible, bad, okay, good, or great"" - -2. Randomly mutate the different parts: - -""In this task, you are given movie reviews"" -> ""You will be presented with a movie review"" -""Categorize the movie review provided"" -> ""Classify the sentiment expressed in"" -""Based on it, classify it to",0.2 -3,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -3,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -4,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -4,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -4,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -4,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -4,"Let's follow the instructions step-by-step: - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. -Prompt 2: Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great. - -Different parts: -""In this task, you are given movie reviews"" vs ""Categorize the movie review provided"" -""Based on it, classify it to one of the five classes"" vs ""into one of five degrees based on the sentiment"" -""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" vs ""terrible, bad, okay, good, or great"" - -2. Randomly mutate the different parts: - -""In this task, you are given movie reviews"" -> ""You will be presented with a movie review"" -""Categorize the movie review provided"" -> ""Classify the sentiment expressed in"" -""Based on it, classify it to",0.2 -4,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -4,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -5,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -5,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -5,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -5,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -5,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -5,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -5,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -5,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -6,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -6,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -6,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -6,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -6,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -6,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -6,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -6,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -7,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -7,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -7,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -7,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -7,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -7,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -7,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -7,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -7,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -8,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -8,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -8,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -8,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -8,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -8,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -8,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -8,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -8,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -9,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -9,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -9,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -9,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -9,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -9,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -9,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -9,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -9,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -10,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -10,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -10,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -10,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -10,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -10,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -10,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -10,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -10,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -11,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -11,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -11,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -11,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -11,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -11,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -11,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -11,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -11,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -12,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.55 -12,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -12,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -12,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -12,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -12,"Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.",0.7 -12,"You will be presented with a movie review. Rate the review according to its sentiment and classify the sentiment expressed in it into one of the following categories: terrible, bad, neutral, good, or great. Provide the classification result.",0.35 -12,"Let's follow the instruction step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. -Prompt 2: Let's follow the instructions step-by-step to generate a better prompt. - -Different parts: -* ""Analyze the sentence"" vs ""Let's follow the instructions"" -* ""categorize it into one of five categories"" vs no equivalent phrase -* ""based on the sentiment: terrible, bad, okay, good, or great"" vs no equivalent phrase - -**Step 2: Randomly mutate the different parts** - -* ""Analyze the sentence"" -> ""Evaluate the review"" -* ""categorize it into one of five categories"" -> ""assign a sentiment rating"" -* ""based on the sentiment: terrible, bad, okay, good, or great"" -> ""from the range of terrible to great"" -* ""Let's follow the instructions"" -> ""Your task is to"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Sentiment",0.25 -12,"Conduct a sentiment analysis to examine the review and assign a rating into a sentiment category: terrible, bad, okay, good, or great. Provide the classification result.",0.45 diff --git a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv b/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv deleted file mode 100644 index 68b74ea..0000000 --- a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv +++ /dev/null @@ -1,200 +0,0 @@ -step,prompt,score -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -1,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -1,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -1,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -1,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -1,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.05 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great. -Prompt 2: Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great. - -Different parts: -""Based on the given movie review"" vs ""Your responsibility is to categorize the movie review"" -""and classify"" vs ""into one of five categories based on the sentiment"" - -**Step 2: Randomly mutate the different parts** - -""Based on the given movie review"" -> ""Considering the provided movie review content"" -""and classify"" -> ""to assign a sentiment label"" -""Your responsibility is to categorize"" -> ""Your task involves evaluating"" -""into one of five categories based on the sentiment"" -> ""according to its emotional tone"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Your objective is to analyze the movie review and allocate it to one of five categories",0.1 -1,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.0 -2,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -2,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -2,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -2,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -2,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -2,"Let's go through the steps to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great. -Prompt 2: Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great. - -Different parts: -""Based on the given movie review"" vs ""Your responsibility is to categorize the movie review"" -""and classify"" vs ""into one of five categories based on the sentiment"" - -**Step 2: Randomly mutate the different parts** - -""Based on the given movie review"" -> ""Considering the provided movie review content"" -""and classify"" -> ""to assign a sentiment label"" -""Your responsibility is to categorize"" -> ""Your task involves evaluating"" -""into one of five categories based on the sentiment"" -> ""according to its emotional tone"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Your objective is to evaluate the sentiment of the movie review and categorize it into one of",0.15 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great. -Prompt 2: Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great. - -Different parts: -""Based on the given movie review"" vs ""Your responsibility is to categorize the movie review"" -""and classify"" vs ""into one of five categories based on the sentiment"" - -**Step 2: Randomly mutate the different parts** - -""Based on the given movie review"" -> ""Considering the provided movie review content"" -""and classify"" -> ""to assign a sentiment label"" -""Your responsibility is to categorize"" -> ""Your task involves evaluating"" -""into one of five categories based on the sentiment"" -> ""according to its emotional tone"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Your objective is to analyze the movie review and allocate it to one of five categories",0.1 -2,"Let's follow the instructions. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: Based on the movie review provided, assign the movie to one of five categories: terrible, bad, okay, good, or great. -Prompt 2: Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text. - -Different parts: -""Based on the movie review provided"" vs ""Please perform Sentiment Classification task. Given the sentence"" -""assign the movie"" vs ""assign a sentiment label"" -""one of five categories"" vs ""from ['terrible', 'bad', 'okay', 'good', 'great']"" -""(no additional instruction)"" vs ""Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Based on the movie review provided"" -> ""Considering the review content"" -""Please perform Sentiment Classification task. Given the sentence"" -> ""Analyze the sentence for sentiment"" -""assign the movie"" -> ""categorize the review"" -""one of five categories"" -> ""into one of five sentiment levels"" -""(no additional",0.15 -3,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -3,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -3,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -3,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -3,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -3,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -3,"Your task involves evaluating the provided movie review content to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.3 -3,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -4,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -4,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -4,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -4,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -4,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -4,"Your task involves evaluating the provided movie review content to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.3 -4,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -5,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -5,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -5,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -5,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -5,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -5,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -5,"Your task involves evaluating the provided movie review content to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.3 -5,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -6,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -6,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -6,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -6,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -6,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -6,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -6,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -6,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -7,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -7,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -7,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -7,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -7,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -7,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -7,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -7,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -7,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -8,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -8,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -8,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -8,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -8,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -8,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -8,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -8,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -8,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -9,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -9,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -9,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -9,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -9,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -9,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -9,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -9,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -9,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -10,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -10,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -10,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -10,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -10,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -10,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -10,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -10,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -10,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -11,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -11,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -11,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -11,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -11,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -11,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -11,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -11,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -11,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 -12,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -12,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.5 -12,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -12,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.5 -12,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -12,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -12,"Considering the provided movie review content, your task involves evaluating its emotional tone to assign a sentiment label according to one of the following categories: terrible, bad, okay, good, or great.",0.4 -12,"Considering the provided review content, evaluate the sentence to assign a sentiment label according to its emotional tone and allocate it to one of five categories: terrible, bad, okay, good, or great.",0.4 -12,"Considering the review content, analyze the sentence for sentiment and categorize the review into one of five sentiment levels: terrible, bad, okay, good, or great.",0.35 diff --git a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv b/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv deleted file mode 100644 index 8f18259..0000000 --- a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -1,"Determine the sentiment of the movie review and choose from terrible, bad, okay, good and great to describe the movie.",0.5 -1,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -1,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -1,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -1,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -1,"Considering the provided movie review content, your objective is to analyze it and assign a rating from among",0.15 -2,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -2,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -2,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -2,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -2,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -2,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -2,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -2,"Considering the provided movie review content, your objective is to analyze it and assign a rating from among",0.15 -3,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -3,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -3,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -3,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -3,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -3,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -3,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -3,"Considering the provided movie review content, your objective is to analyze it and assign a rating from among",0.15 -4,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -4,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -4,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -4,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -4,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -4,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -4,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -5,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -5,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -5,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -5,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -5,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -5,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -5,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -5,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -6,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -6,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -6,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -6,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -6,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -6,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -6,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -6,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -7,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -7,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -7,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -7,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -7,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -7,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -7,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -7,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -7,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -8,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -8,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -8,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -8,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -8,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -8,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -8,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -8,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -8,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -9,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -9,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -9,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -9,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -9,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -9,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -9,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -9,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -9,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -10,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -10,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -10,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -10,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -10,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -10,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -10,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -10,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -10,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -11,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -11,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -11,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -11,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -11,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -11,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -11,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -11,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -11,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 -12,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -12,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.55 -12,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.5 -12,"Evaluate the given movie review snippet and assign a sentiment label from the following options: terrible, bad, okay, good, or great, describing the overall sentiment of the movie.",0.55 -12,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -12,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -12,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.4 -12,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.35 -12,"Considering the provided piece of text, analyze the rating of the review and assign a rating from among terrible,",0.25 diff --git a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index b566d4a..0000000 --- a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,336 +0,0 @@ -step,prompt,score -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -1,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -1,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -1,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -1,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text."" - -Different parts: - -* ""Analyze the sentence"" vs ""Please perform Sentiment Classification task"" -* ""and categorize it into one of five categories based on the sentiment"" vs ""assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']"" -* ""sentence"" vs ""sentence"" -* ""one of five categories based on the sentiment"" vs ""sentiment label"" - -Note that the prompts are quite similar, but there are some differences in wording and formatting. - -2. Randomly mutate the different parts: - -* ""Analyze the sentence"" -> ""Evaluate the input text"" -* ""and categorize it into one of five categories based on the sentiment"" -> ""and determine its overall sentiment",0.2 -1,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.1 -1,"Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.",0.0 -2,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -2,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -2,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -2,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -2,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -2,"I'd be happy to help you generate a better prompt! - -Here are the steps: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text."" - -Different parts: - -* ""Analyze the sentence"" vs ""Please perform Sentiment Classification task"" -* ""and categorize it into one of five categories based on the sentiment"" vs ""assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']"" -* ""sentence"" vs ""sentence"" -* ""one of five categories based on the sentiment"" vs ""sentiment label"" - -2. Randomly mutate the different parts: - -* ""Analyze the sentence"" -> ""Examine the text input"" -* ""and categorize it into one of five categories based on the sentiment"" -> ""and classify its emotional tone"" - -3. Crossover the different parts with the following Prompt 3",0.25 -2,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.1 -2,and,0.15 -3,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -3,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -3,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -3,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -3,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -3,"I'd be happy to help you generate a better prompt! - -Here are the steps: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text."" - -Different parts: - -* ""Analyze the sentence"" vs ""Please perform Sentiment Classification task"" -* ""and categorize it into one of five categories based on the sentiment"" vs ""assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']"" -* ""sentence"" vs ""sentence"" -* ""one of five categories based on the sentiment"" vs ""sentiment label"" - -2. Randomly mutate the different parts: - -* ""Analyze the sentence"" -> ""Examine the text input"" -* ""and categorize it into one of five categories based on the sentiment"" -> ""and classify its emotional tone"" - -3. Crossover the different parts with the following Prompt 3",0.25 -3,"Your goal is to analyze the movie review and assign it to one of five categories, ranging from terrible to great.",0.1 -3,and,0.15 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -4,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -4,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -4,"Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -4,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -4,"I'd be happy to help you generate a better prompt! - -Here are the steps: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text."" - -Different parts: - -* ""Analyze the sentence"" vs ""Please perform Sentiment Classification task"" -* ""and categorize it into one of five categories based on the sentiment"" vs ""assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']"" -* ""sentence"" vs ""sentence"" -* ""one of five categories based on the sentiment"" vs ""sentiment label"" - -2. Randomly mutate the different parts: - -* ""Analyze the sentence"" -> ""Examine the text input"" -* ""and categorize it into one of five categories based on the sentiment"" -> ""and classify its emotional tone"" - -3. Crossover the different parts with the following Prompt 3",0.25 -4,"Examine the passage and categorize it into one of the following categories based on its sentiment: terrible, bad, okay, good, great, while considering the context and tone of the movie",0.35 -4,and,0.15 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -5,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -5,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -5,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -5,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -5,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -5,"I'd be happy to help you generate a better prompt! - -Here are the steps: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great."" -Prompt 2: ""Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text."" - -Different parts: - -* ""Analyze the sentence"" vs ""Please perform Sentiment Classification task"" -* ""and categorize it into one of five categories based on the sentiment"" vs ""assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']"" -* ""sentence"" vs ""sentence"" -* ""one of five categories based on the sentiment"" vs ""sentiment label"" - -2. Randomly mutate the different parts: - -* ""Analyze the sentence"" -> ""Examine the text input"" -* ""and categorize it into one of five categories based on the sentiment"" -> ""and classify its emotional tone"" - -3. Crossover the different parts with the following Prompt 3",0.25 -5,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -5,"Task is to classify the review based on its sentimental tone of a movie review. Given the sentence, assign a sentiment label from ['terrible', 'bad', '",0.3 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.4 -6,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -6,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -6,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -6,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -6,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -6,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -6,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -6,"Task is to classify the review based on its sentimental tone of a movie review. Given the sentence, assign a sentiment label from ['terrible', 'bad', '",0.3 -7,"Here are the different parts between Prompt 1 and Prompt 2: - -* ""Categorize the movie review"" vs ""The task is to assess the movie critique"" -* ""one of five degrees based on the sentiment"" vs ""examining the quote and categorizing it into one of the following categories"" -* ""terrible, bad, okay, good, or great"" vs ""terrible, bad, okay, good, or great"" (same phrase) -* ""taking into consideration the tone"" (only present in Prompt 2) - -Let me know when to proceed with the next steps!",0.45 -7,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -7,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -7,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -7,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -7,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -7,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -7,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -7,"Task is to classify the review based on its sentimental tone of a movie review. Given the sentence, assign a sentiment label from ['terrible', 'bad', '",0.3 -8,"Here are the different parts between Prompt 1 and Prompt 2: - -* ""Categorize the movie review"" vs ""The task is to assess the movie critique"" -* ""one of five degrees based on the sentiment"" vs ""examining the quote and categorizing it into one of the following categories"" -* ""terrible, bad, okay, good, or great"" vs ""terrible, bad, okay, good, or great"" (same phrase) -* ""taking into consideration the tone"" (only present in Prompt 2) - -Let me know when to proceed with the next steps!",0.45 -8,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -8,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -8,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -8,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -8,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -8,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -8,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -8,"Task is to classify the review based on its sentimental tone of a movie review. Given the sentence, assign a sentiment label from ['terrible', 'bad', '",0.3 -9,"Here are the different parts between Prompt 1 and Prompt 2: - -* ""Categorize the movie review"" vs ""The task is to assess the movie critique"" -* ""one of five degrees based on the sentiment"" vs ""examining the quote and categorizing it into one of the following categories"" -* ""terrible, bad, okay, good, or great"" vs ""terrible, bad, okay, good, or great"" (same phrase) -* ""taking into consideration the tone"" (only present in Prompt 2) - -Let me know when to proceed with the next steps!",0.45 -9,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -9,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -9,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -9,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -9,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -9,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -9,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -9,"Task is to classify the review based on its sentimental tone of a movie review. Given the sentence, assign a sentiment label from ['terrible', 'bad', '",0.3 -10,"Here are the different parts between Prompt 1 and Prompt 2: - -* ""Categorize the movie review"" vs ""The task is to assess the movie critique"" -* ""one of five degrees based on the sentiment"" vs ""examining the quote and categorizing it into one of the following categories"" -* ""terrible, bad, okay, good, or great"" vs ""terrible, bad, okay, good, or great"" (same phrase) -* ""taking into consideration the tone"" (only present in Prompt 2) - -Let me know when to proceed with the next steps!",0.45 -10,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -10,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -10,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -10,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -10,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -10,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -10,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -10,"Analyze the review's sentiment and determine the emotional sentiment from the following sentiment options: terrible, bad, okay, good, great, considering the tone and context of the movie review.",0.4 -11,"Here are the different parts between Prompt 1 and Prompt 2: - -* ""Categorize the movie review"" vs ""The task is to assess the movie critique"" -* ""one of five degrees based on the sentiment"" vs ""examining the quote and categorizing it into one of the following categories"" -* ""terrible, bad, okay, good, or great"" vs ""terrible, bad, okay, good, or great"" (same phrase) -* ""taking into consideration the tone"" (only present in Prompt 2) - -Let me know when to proceed with the next steps!",0.45 -11,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -11,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -11,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.35 -11,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -11,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -11,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -11,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -11,"Analyze the review's sentiment and determine the emotional sentiment from the following sentiment options: terrible, bad, okay, good, great, considering the tone and context of the movie review.",0.4 -12,"Here are the different parts between Prompt 1 and Prompt 2: - -* ""Categorize the movie review"" vs ""The task is to assess the movie critique"" -* ""one of five degrees based on the sentiment"" vs ""examining the quote and categorizing it into one of the following categories"" -* ""terrible, bad, okay, good, or great"" vs ""terrible, bad, okay, good, or great"" (same phrase) -* ""taking into consideration the tone"" (only present in Prompt 2) - -Let me know when to proceed with the next steps!",0.45 -12,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.4 -12,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.35 -12,"Classify the movie review provided into one of the following categories: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great, considering the sentiment expressed in the review.",0.45 -12,"To accurately classify paragraphs in a review, analyze the sentence according to sentiment and categorize it into one of the following categories: terrible, bad, okay, good",0.4 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.3 -12,"The task is to assess the movie critique, examining the quote and categorizing it into one of the following categories: terrible, bad, okay, good, or great, taking into consideration the tone.",0.45 -12,"Here is the revised prompt: - -Examine the text input and classify its emotional tone as one of the following categories: terrible, bad, okay, good, great. - -Comparison to Prompt 3: - -* ""Examine the text input"" is similar to ""I'd be happy to help you generate a better prompt!"" in terms of tone and phrasing, although the context is different. -* ""and classify its emotional tone"" is similar to the task of sentiment classification described in Prompt 3, with the addition of a more descriptive phrase. -* ""one of the following categories"" is similar to the list of categories provided in Prompt 3, with the exact same categories. - -Overall, the revised prompt is more concise and focused on the task of sentiment classification, while also providing a clearer sense of what is being asked of the user.",0.45 -12,"Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.",0.5 -12,"Analyze the review's sentiment and determine the emotional sentiment from the following sentiment options: terrible, bad, okay, good, great, considering the tone and context of the movie review.",0.4 diff --git a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 4dcac08..0000000 --- a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,349 +0,0 @@ -step,prompt,score -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -1,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -1,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -1,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -1,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -1,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.15 -1,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -1,and,0.1 -2,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -2,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -2,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -2,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -2,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -2,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -2,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.15 -2,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -2,"Take into account the movie review context and label the reviewed film into one of the following categories: terrible, bad, okay, good",0.25 -3,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -3,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -3,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -3,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -3,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -3,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -3,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.15 -3,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -3,"Analyze the movie review and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.4 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -4,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -4,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -4,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -4,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -4,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -4,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.15 -4,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -4,"Analyze the movie review and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.4 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -5,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -5,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -5,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -5,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -5,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -5,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.15 -5,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -5,"Analyze the movie review and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.4 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -6,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -6,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -6,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -6,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -6,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -6,"Your objective is to evaluate the the sentiment of the movie review and categorize it into one of five classes, ranging from terrible to great.",0.15 -6,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -6,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 -7,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -7,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -7,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -7,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -7,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -7,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -7,"Assess the opinion expressed in movie reviews and assign it to one of five ratings: terrible, bad, okay, good, or great, while considering the overall sentiment.",0.6 -7,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -7,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 -8,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -8,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -8,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -8,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -8,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -8,"Examine the comment provided and classify it into one of five categories: terrible, bad, okay, good, or great.",0.25 -8,"Assess the opinion expressed in movie reviews and assign it to one of five ratings: terrible, bad, okay, good, or great, while considering the overall sentiment.",0.6 -8,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -8,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 -9,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -9,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -9,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -9,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -9,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -9,"Evaluate the final assessment task by examining critical evaluations in movie reviews and render a subjective judgment one of five ratings: terrible, bad, okay, good, or great.",0.45 -9,"Assess the opinion expressed in movie reviews and assign it to one of five ratings: terrible, bad, okay, good, or great, while considering the overall sentiment.",0.6 -9,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -9,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 -10,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -10,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -10,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -10,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -10,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -10,"Evaluate the final assessment task by examining critical evaluations in movie reviews and render a subjective judgment one of five ratings: terrible, bad, okay, good, or great.",0.45 -10,"Assess the opinion expressed in movie reviews and assign it to one of five ratings: terrible, bad, okay, good, or great, while considering the overall sentiment.",0.6 -10,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -10,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 -11,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -11,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -11,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -11,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -11,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -11,"Evaluate the final assessment task by examining critical evaluations in movie reviews and render a subjective judgment one of five ratings: terrible, bad, okay, good, or great.",0.45 -11,"Assess the opinion expressed in movie reviews and assign it to one of five ratings: terrible, bad, okay, good, or great, while considering the overall sentiment.",0.6 -11,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -11,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 -12,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.6 -12,"Based on the movie review provided, assign the moviw to one of five categories: terrible, bad, okay, good, or great.",0.6 -12,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.6 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.55 -12,"Your responsibility is to categorize the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -12,"Based on the given movie review, classify it into in one of the five categories: terrible, bad, okay, good, or great.",0.45 -12,"Consider the content of the movie review available and render a subjective judgment one of five ratings: terrible, bad, okay, good, or great, taking into account the relevant context.",0.55 -12,"Assess the opinion expressed in movie reviews and assign it to one of five ratings: terrible, bad, okay, good, or great, while considering the overall sentiment.",0.6 -12,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: ""Your responsibility is to categorize the movie review provided to you"" vs ""Your are given movie reviews"". -""to one of five categories based on the sentiment"" vs ""classify it to one of the five classes"". -""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"". - -Different parts: - -* ""Your responsibility is to categorize"" vs ""Your objectiv"" -* ""the movie review provided to you"" vs ""movie reviews"" -* ""to one of five categories based on the sentiment"" vs ""to one of the following classes"" -* ""terrible, bad, okay, good, or great"" vs ""(1) terrible, (2) bad, (3) okay, (4) good, and (5) great"" - -2. Randomly mutate the different parts: - -* ""Your responsibility is to categorize"" -> ""The goal is to identify"" -* ""the movie review provided to you"" -> ""a movie review text"" -* ""to one of",0.4 -12,"Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.",0.65 diff --git a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 42d418d..0000000 --- a/logs/experiment/sst-5_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your job is to evaluate the review and classify it as one of five classes: terrible, bad, okay, good, or great.",0.45 -1,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.45 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.",0.4 -1,"In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.",0.4 -1,"Determine the sentiment of the movie review and choose from terrible, bad, okay, good and great to describe the movie.",0.4 -1,"Examine the film analysis based on the sentiment and categorize it into one of the following categories: terrible, bad, okay, good, or great, while considering the overall sentiment. - -The final prompt aims to clarify the task and provide a more comprehensive understanding of what the classifier should focus on.",0.5 -3,"Classify the provided text according to its sentiment, categorizing it as terrible, bad, okay, good, or great.",0.45 -3,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -3,"Based on the given movie review provided to you and classify it into one of five categories: terrible, bad, okay, good, or great.",0.45 -4,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -4,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -4,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -4,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -4,"Classify the given review as 'terrible', 'bad', 'okay', 'good', or 'great', and return the sentiment label.",0.5 -4,"Classify the given movie reviews into one of the five sentiment categories: terrible, bad, okay, good, or great, using linguistic features to assess their tone and convey the viewer's emotional response.",0.5 -4,"Assess the movie review based on its sentiment and overall quality, categorizing it into one of five levels: terrible, bad, okay, good, or great, considering both emotional tone and objective value.",0.5 -4,"<Classify the movie review into one of five categories (terrible, bad, okay, good, or great) based on both its sentiment and overall quality</prompt> - -The final prompt aims to clarify the task and provide a more comprehensive understanding of what the classifier should focus on.",0.5 -4,"Classify the provided text according to its sentiment, categorizing it as terrible, bad, okay, good, or great.",0.45 -4,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -5,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -5,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -5,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -5,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -5,"Classify the given review as 'terrible', 'bad', 'okay', 'good', or 'great', and return the sentiment label.",0.5 -5,"Classify the given movie reviews into one of the five sentiment categories: terrible, bad, okay, good, or great, using linguistic features to assess their tone and convey the viewer's emotional response.",0.5 -5,"Assess the movie review based on its sentiment and overall quality, categorizing it into one of five levels: terrible, bad, okay, good, or great, considering both emotional tone and objective value.",0.5 -5,"<Classify the movie review into one of five categories (terrible, bad, okay, good, or great) based on both its sentiment and overall quality</prompt> - -The final prompt aims to clarify the task and provide a more comprehensive understanding of what the classifier should focus on.",0.5 -5,"Classify the provided text according to its sentiment, categorizing it as terrible, bad, okay, good, or great.",0.45 -5,"Categorize the movie review provided into one of five degrees based on the sentiment: terrible, bad, okay, good, or great.",0.45 -6,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -6,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -6,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -6,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -6,"Classify the sentiment of the given text into one of five categories - 'terrible', 'bad', 'okay', 'good', or 'great' - based on its tone and linguistic characteristics.",0.5 -6,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -6,"Classify the given review as 'terrible', 'bad', 'okay', 'good', or 'great', and return the sentiment label.",0.5 -6,"Classify the given movie reviews into one of the five sentiment categories: terrible, bad, okay, good, or great, using linguistic features to assess their tone and convey the viewer's emotional response.",0.5 -6,"Assess the movie review based on its sentiment and overall quality, categorizing it into one of five levels: terrible, bad, okay, good, or great, considering both emotional tone and objective value.",0.5 -6,"<Classify the movie review into one of five categories (terrible, bad, okay, good, or great) based on both its sentiment and overall quality</prompt> - -The final prompt aims to clarify the task and provide a more comprehensive understanding of what the classifier should focus on.",0.5 -7,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -7,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -7,"Assess the sentiment of the text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and linguistic qualities.",0.6 -7,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -7,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -7,"Evaluate the sentiment of the provided text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone, linguistic features, and overall emotional impact.",0.5 -7,"Evaluate the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great'",0.5 -7,"Determine the sentiment of the provided text by classifying it into 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, and output the relevant sentiment label.",0.5 -7,"Classify the sentiment of the given text into one of five categories - 'terrible', 'bad', 'okay', 'good', or 'great' - based on its tone and linguistic characteristics.",0.5 -7,"Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.",0.5 -8,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -8,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -8,"Assess the sentiment of the text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and linguistic qualities.",0.6 -8,"Evaluate the sentiment of the provided text and determine its relevance as 'terrible', 'bad', 'okay', 'good', or 'great', returning the corresponding label.",0.55 -8,"Evaluate the emotional tone of the provided text, identifying its sentiment as 'terrible', 'bad', 'okay', 'good', or 'great' based on linguistic features, and categorize it accordingly.",0.55 -8,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -8,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -8,"Evaluate the sentiment of the provided text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone, linguistic features, and overall emotional impact.",0.5 -8,"Evaluate the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great'",0.5 -8,"Determine the sentiment of the provided text by classifying it into 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, and output the relevant sentiment label.",0.5 -9,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -9,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -9,"Assess the sentiment of the text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and linguistic qualities.",0.6 -9,"Evaluate the sentiment of the provided text and determine its relevance as 'terrible', 'bad', 'okay', 'good', or 'great', returning the corresponding label.",0.55 -9,"Evaluate the emotional tone of the provided text, identifying its sentiment as 'terrible', 'bad', 'okay', 'good', or 'great' based on linguistic features, and categorize it accordingly.",0.55 -9,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -9,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -9,"Evaluate the sentiment of the provided text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone, linguistic features, and overall emotional impact.",0.5 -9,"Evaluate the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great'",0.5 -9,"Determine the sentiment of the provided text by classifying it into 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, and output the relevant sentiment label.",0.5 -10,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -10,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -10,"Assess the sentiment of the text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and linguistic qualities.",0.6 -10,"Evaluate the sentiment of the provided text and determine its relevance as 'terrible', 'bad', 'okay', 'good', or 'great', returning the corresponding label.",0.55 -10,"Evaluate the emotional tone of the provided text, identifying its sentiment as 'terrible', 'bad', 'okay', 'good', or 'great' based on linguistic features, and categorize it accordingly.",0.55 -10,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -10,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -10,"Evaluate the sentiment of the provided text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone, linguistic features, and overall emotional impact.",0.5 -10,"Evaluate the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great'",0.5 -10,"Determine the sentiment of the provided text by classifying it into 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, and output the relevant sentiment label.",0.5 -11,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -11,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -11,"Assess the sentiment of the text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and linguistic qualities.",0.6 -11,"Evaluate the sentiment of the provided text and determine its relevance as 'terrible', 'bad', 'okay', 'good', or 'great', returning the corresponding label.",0.55 -11,"Evaluate the emotional tone of the provided text, identifying its sentiment as 'terrible', 'bad', 'okay', 'good', or 'great' based on linguistic features, and categorize it accordingly.",0.55 -11,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -11,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -11,"Evaluate the sentiment of the provided text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone, linguistic features, and overall emotional impact.",0.5 -11,"Evaluate the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great'",0.5 -11,"Determine the sentiment of the provided text by classifying it into 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, and output the relevant sentiment label.",0.5 -12,"Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.",0.65 -12,"Determine the emotional tone of the provided text and categorize it as either 'terrible', 'bad', 'okay', 'good', or 'great' based on its linguistic characteristics.",0.6 -12,"Assess the sentiment of the text, categorizing it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and linguistic qualities.",0.6 -12,"Evaluate the sentiment of the provided text and determine its relevance as 'terrible', 'bad', 'okay', 'good', or 'great', returning the corresponding label.",0.55 -12,"Evaluate the emotional tone of the provided text, identifying its sentiment as 'terrible', 'bad', 'okay', 'good', or 'great' based on linguistic features, and categorize it accordingly.",0.55 -12,"Classify the input text as 'terrible', 'bad', 'okay', 'good', or 'great' based on its sentiment, returning a sentiment label.",0.55 -12,"Classify the input sentence and categorize its sentiment into one of the following labels: terrible, bad, okay, good, or great. Return the sentiment label as the output without any additional text.",0.55 -12,"Assess the emotional tone and sentiment of the input text, and evaluate its linguistic characteristics to categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' in terms of its overall emotional impact.",0.55 -12,"Evaluate the sentiment of the provided text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone, linguistic features, and overall emotional impact.",0.5 -12,"Evaluate the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great'",0.5 diff --git a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv b/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv deleted file mode 100644 index 243f422..0000000 --- a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -1,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -1,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -1,"Determine the sentiment of the text provided, positive or negative.",0.95 -1,Classify a sentence as expressing optimism or pessimism.,0.95 -1,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -1,"When given a social media post, you need to determine the sentiment of the message by analyzing",0.8 -2,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -2,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -2,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -2,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -2,"Determine the sentiment of the text provided, positive or negative.",0.95 -2,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -2,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -2,"When given a social media post, you need to determine the sentiment of the message by analyzing",0.8 -3,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -3,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -3,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -3,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -3,"Determine the sentiment of the text provided, positive or negative.",0.95 -3,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -3,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -3,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -3,"When given a social media post, you need to determine the sentiment of the message by analyzing",0.8 -4,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -4,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -4,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -4,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -4,"Determine the sentiment of the text provided, positive or negative.",0.95 -4,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -4,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -4,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -4,"When given a social media post, you need to determine the sentiment of the message by analyzing",0.8 -5,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -5,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -5,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -5,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -5,"Determine the sentiment of the text provided, positive or negative.",0.95 -5,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -5,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -5,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -5,"When given a social media post, you need to determine the sentiment of the message by analyzing",0.8 -6,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -6,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -6,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -6,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -6,"Determine the sentiment of the text provided, positive or negative.",0.95 -6,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -6,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -6,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -6,"When given social media post content, analyze the tone of the given message by considering all relevant factors and classify it as ""positive"" or ""negative"".",0.85 -7,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -7,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -7,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -7,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -7,"Determine the sentiment of the text provided, positive or negative.",0.95 -7,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -7,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -7,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -7,"When given social media post content, analyze the tone of the given message by considering all relevant factors and classify it as ""positive"" or ""negative"".",0.85 -8,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -8,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -8,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -8,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -8,"Determine the sentiment of the text provided, positive or negative.",0.95 -8,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -8,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -8,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -8,"When given social media post content, analyze the tone of the given message by considering all relevant factors and classify it as ""positive"" or ""negative"".",0.85 -9,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -9,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -9,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -9,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -9,"Determine the sentiment of the text provided, positive or negative.",0.95 -9,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -9,"Analyze a piece of text and assign a label of positive or negative based on the sentiment, assessing the emotional tone of a provided statement.",0.9 -9,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -9,"When given social media post content, analyze the tone of the given message by considering all relevant factors and classify it as ""positive"" or ""negative"".",0.85 -10,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -10,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -10,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -10,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -10,"Determine the sentiment of the text provided, positive or negative.",0.95 -10,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -10,"Analyze a written message and assign a label of positive or negative based on the sentiment, by evaluating the emotional tone and content of the passage.",0.95 -10,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -10,"When given social media post content, analyze the tone of the given message by considering all relevant factors and classify it as ""positive"" or ""negative"".",0.85 -11,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -11,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -11,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -11,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -11,"Determine the sentiment of the text provided, positive or negative.",0.95 -11,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -11,"Analyze a written message and assign a label of positive or negative based on the sentiment, by evaluating the emotional tone and content of the passage.",0.95 -11,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -11,"When given social media post content, analyze the tone of the given message by considering all relevant factors and classify it as ""positive"" or ""negative"".",0.85 -12,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -12,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",1.0 -12,"Given a tweet, classify it as having a positive or negative sentiment.",1.0 -12,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -12,"Determine the sentiment of the text provided, positive or negative.",0.95 -12,"Analyze the input sentence or phrase and assess the emotional tone as expressing either optimism, pessimism, positive sentiment, or negative sentiment, while evaluating the emotional leaning of the text.",1.0 -12,"Analyze a written message and assign a label of positive or negative based on the sentiment, by evaluating the emotional tone and content of the passage.",0.95 -12,"As a sentiment classifier, examine user-generated content and identify the sentiment of a statement as positive or negative, while considering the meaning and relevant context.",0.85 -12,"Examine the message, assessing the tone by considering all relevant factors, and classify it as ""positive"" or ""negative"" sentiment.",0.95 diff --git a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv b/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv deleted file mode 100644 index 4eec44f..0000000 --- a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -1,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -1,Classify a sentence as expressing optimism or pessimism.,0.85 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -1,"When given a social media post, determine whether it contains a positive or negative sentiment by analyzing it and generating a sentiment word to describe the original text.",0.85 -1,"In this task, you are given sentences from movie reviews. The task is to classify a sentence as positive or as negative.",0.75 -2,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -2,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -2,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -2,Classify a sentence as expressing optimism or pessimism.,0.85 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -2,"When given a social media post, determine whether it contains a positive or negative sentiment by analyzing it and generating a sentiment word to describe the original text.",0.85 -2,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -3,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -3,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -3,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -3,Classify a sentence as expressing optimism or pessimism.,0.85 -3,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.8 -3,"When given a social media post, determine whether it contains a positive or negative sentiment by analyzing it and generating a sentiment word to describe the original text.",0.85 -3,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -4,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -4,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -4,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -4,Classify a sentence as expressing optimism or pessimism.,0.85 -4,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -4,"When given a social media post, determine whether it contains a positive or negative sentiment by analyzing it and generating a sentiment word to describe the original text.",0.85 -4,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -5,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -5,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -5,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -5,Classify a sentence as expressing optimism or pessimism.,0.85 -5,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -5,"Provided with a post, examine the message and determine the sentiment as favorable or unfavorable",0.9 -5,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -6,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -6,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -6,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -6,Classify a sentence as expressing optimism or pessimism.,0.85 -6,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -6,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -6,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -7,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -7,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -7,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -7,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -7,Classify a sentence as expressing optimism or pessimism.,0.85 -7,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -7,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -7,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -8,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -8,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -8,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -8,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -8,Classify a sentence as expressing optimism or pessimism.,0.85 -8,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -8,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -8,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -9,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -9,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -9,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -9,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -9,Classify a sentence as expressing optimism or pessimism.,0.85 -9,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -9,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -9,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -10,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -10,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -10,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -10,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -10,Classify a sentence as expressing optimism or pessimism.,0.85 -10,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -10,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -10,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -11,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -11,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -11,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -11,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -11,Classify a sentence as expressing optimism or pessimism.,0.85 -11,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -11,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -11,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 -12,"Given a sentence, classify it as either positive or negative sentiment.",1.0 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",0.95 -12,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.95 -12,"Given a tweet, classify it as having a positive or negative sentiment.",0.95 -12,"Given a statement, classify it as expressing a positive or negative opinion.",0.9 -12,Classify a sentence as expressing optimism or pessimism.,0.85 -12,"As a sentiment classifier, evaluate the text and determine the tone of a statement as expressing emotions of joy or sadness, by first understanding its meaning",0.85 -12,"Analyzing a social media post, examine the message and determine the attitude as hopeful or despairing, favorable or unfavorable.",0.95 -12,"In this task, you are given text snippets from movie reviews. You need to categorize them as either negative or positive",0.85 diff --git a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv b/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv deleted file mode 100644 index 61f492f..0000000 --- a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -1,"Determine the sentiment of a movie review based on a given text, positive or negative.",0.9 -1,"Based on the input message, assess the emotional response in the statement and assign a sentiment label from ['negative', 'positive'].",0.9 -1,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -1,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.75 -1,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -1,"Given text, classify whether it is positive or negative.",0.6 -1,"Determine the sentiment of the text provided, positive or negative.",0.6 -2,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.9 -2,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -2,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -2,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -2,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.75 -2,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -2,"Given text, classify whether it is positive or negative.",0.6 -2,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -3,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -3,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -3,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -3,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -3,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -3,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -3,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.75 -3,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -3,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -3,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -4,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -4,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -4,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -4,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -4,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -4,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -4,"Now you are a classifier, your task is to determine the sentiment polarity of the given text, whether positive or negative.",0.75 -4,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -4,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -4,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -5,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -5,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -5,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -5,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -5,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -5,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -5,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -5,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -5,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -5,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -6,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -6,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -6,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -6,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -6,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -6,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -6,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -6,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -6,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -6,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -7,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -7,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -7,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -7,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -7,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -7,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -7,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -7,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -7,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -7,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -8,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -8,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -8,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -8,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -8,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -8,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -8,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -8,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -8,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -8,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -9,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -9,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -9,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -9,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -9,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -9,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -9,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -9,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -9,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -9,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -10,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -10,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -10,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -10,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -10,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -10,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -10,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -10,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -10,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -10,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -11,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -11,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -11,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -11,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -11,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -11,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -11,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -11,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -11,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -11,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 -12,"Your task is to classify the comment ""positive"" or ""negative"".",0.95 -12,"Examine the passage and assess the emotion expressed as positive or negative, through a thorough review of the language used",0.95 -12,"You are tasked with evaluating the emotional tone of a provided paragraph, and classify it as either positive or negative sentiment.",0.95 -12,"You will analyze the input passage to identify its sentiment, determining whether a review snippet has a positive or negative sentiment.",0.95 -12,"Analyze the input message to assess the emotional response if the sentiment is positive or negative and assign a label accordingly from ['negative', 'positive'].",0.95 -12,You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.,1.0 -12,"You are a classifier, required to evaluate the given text and determine its sentiment polarity as either favorable or unfavorable, by analyzing the",0.9 -12,"As a sentiment classifier, analyze the emotional tone of the provided piece of film criticism, understanding its meaning and relevant context, and classify it as positive or negative.",0.85 -12,You will act as a sentiment analyzer to evaluate the provided statement and classify whether it is positive or negative.,0.9 -12,"Determine the sentiment of the written expression, whether it's positive or negative, by analyzing the content provided.",0.85 diff --git a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 5bf2e19..0000000 --- a/logs/experiment/sst2_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -1,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -1,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -1,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.9 -1,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -1,"Determine the sentiment of the text provided, positive or negative.",0.8 -1,"When given an English sentence, your task is to analyze it and generate a sentiment word to describe the original text, such as positive or negative.",0.75 -1,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.7 -1,"The goal is to conduct a sentiment evaluation by determining whether a sentence expresses optimism or pessimism, or if it falls somewhere in between, considering the polarities of positive and negative.",0.75 -1,Classify a sentence as expressing optimism or pessimism.,0.7 -2,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -2,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -2,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -2,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.9 -2,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -2,"Determine the sentiment of the text provided, positive or negative.",0.8 -2,"Analyze snippets of online content and assign a sentiment label to them as positive, neutral/negative, based on the meaning and tone of the text.",0.8 -2,"Please label the sentiment towards the movie of the given movie review. The sentiment label should be ""positive"" or ""negative"".",0.7 -2,"The goal is to conduct a sentiment evaluation by determining whether a sentence expresses optimism or pessimism, or if it falls somewhere in between, considering the polarities of positive and negative.",0.75 -2,"As a sentiment classifier, analyze the given text and categorize it into optimism or pessimism.",0.9 -3,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -3,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -3,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -3,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.9 -3,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -3,"Determine the sentiment of the text provided, positive or negative.",0.8 -3,"Analyze snippets of online content and assign a sentiment label to them as positive, neutral/negative, based on the meaning and tone of the text.",0.8 -3,"Analyze the emotional tone of the statement, a movie review, and label the sentiment as either positive",0.75 -3,"The goal is to conduct a sentiment evaluation by determining whether a sentence expresses optimism or pessimism, or if it falls somewhere in between, considering the polarities of positive and negative.",0.75 -3,"As a sentiment classifier, analyze the given text and categorize it into optimism or pessimism.",0.9 -4,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -4,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -4,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -4,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.9 -4,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -4,"Determine the sentiment of the text provided, positive or negative.",0.8 -4,"Examine the emotional resonance of paragraphs of online feedback and categorize them as either positive, neutral, or negative",0.85 -4,"Analyze the emotional tone of the statement, a movie review, and label the sentiment as either positive",0.75 -4,"The goal is to conduct a sentiment evaluation by determining whether a sentence expresses optimism or pessimism, or if it falls somewhere in between, considering the polarities of positive and negative.",0.75 -4,"As a sentiment classifier, analyze the given text and categorize it into optimism or pessimism.",0.9 -5,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -5,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -5,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -5,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.9 -5,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -5,"Determine the sentiment of the text provided, positive or negative.",0.8 -5,"Examine the emotional resonance of paragraphs of online feedback and categorize them as either positive, neutral, or negative",0.85 -5,"Analyze the emotional tone of the statement, a movie review, and label the sentiment as either positive",0.75 -5,"The goal is to conduct a sentiment evaluation by determining whether a sentence expresses optimism or pessimism, or if it falls somewhere in between, considering the polarities of positive and negative.",0.75 -5,"As a sentiment classifier, analyze the given text and categorize it into optimism or pessimism.",0.9 -6,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -6,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -6,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -6,"You are a sentiment classifier. To do this, you must first understand the meaning of the sentence and any relevant context. And then you should classify it as positive or negative.",0.9 -6,"Given a tweet, classify it as having a positive or negative sentiment.",0.9 -6,"Determine the sentiment of the text provided, positive or negative.",0.8 -6,"Examine the emotional resonance of paragraphs of online feedback and categorize them as either positive, neutral, or negative",0.85 -6,"Analyze the emotional tone of the statement, a movie review, and label the sentiment as either positive",0.75 -6,"The goal is to conduct a sentiment evaluation by determining whether a sentence expresses optimism or pessimism, or if it falls somewhere in between, considering the polarities of positive and negative.",0.75 -6,"As a sentiment classifier, analyze the given text and categorize it into optimism or pessimism.",0.9 -7,"Your task is to classify the comment ""positive"" or ""negative"".",1.0 -7,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0 -7,"Given a sentence, classify it as either positive or negative sentiment.",0.95 -7,"Analyze the emotional tone of sections of written feedback and classify their sentiment as either positive, both positive and negative, while considering the meaning of the sentence and any relevant context. ""Analyzing a passage"" -""classify it"" -> ""assess the tone"" -""expressing a subjective or objective opinion"" -> ""as either subjective or objective"" - -""Considering a given text"" -> ""Examining a piece of writing"" -""determine the nature... and evaluate it accordingly"" -> ""categorize and judge the sentiment"" -""into objective or subjective categories"" -> ""according to its subjective or objective tone"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Determine the",0.6 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly. -Prompt 2: Your goal is to evaluate every statement as either subjective or objective by categorizing a message according to its tone. - -Different parts: -""Considering a given text"" vs ""Your goal is to evaluate every statement"" -""determine the nature of the sentence or phrase"" vs ""categorizing a message"" -""text"" vs ""statement"" -""sentence or phrase"" vs ""message"" -""into objective or subjective categories, and evaluate it accordingly"" vs ""as either subjective or objective by categorizing... its tone"" - -**Step 2: Randomly mutate the different parts** - -Mutated parts: -""Considering a given text"" -> ""Analyzing a provided passage"" -""determine the nature of the sentence or phrase"" -> ""assess the type of expression"" -""text"" -> ""entry"" -""sentence or phrase"" -> ""piece of writing"" -""into objective or subjective categories, and evaluate it accordingly"" -> ""based on their subjective or objective tone, classify them"" -""",0.55 -4,Your goal is to evaluate every statement as either subjective or objective by categorizing a message according to its tone.,0.55 -5,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -5,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -5,"Assess the given sentence and determine whether it is into subjective or objective opinion, evaluating the given sentences and their sentiment.",0.8 -5,"Your task is to classify the comment ""subjective"" or ""objective"".",0.65 -5,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.6 -5,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -5,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -5,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly. -Prompt 2: Your goal is to evaluate every statement as either subjective or objective by categorizing a message according to its tone. - -Different parts: -""Considering a given text"" vs ""Your goal is to evaluate every statement"" -""determine the nature of the sentence or phrase"" vs ""categorizing a message"" -""text"" vs ""statement"" -""sentence or phrase"" vs ""message"" -""into objective or subjective categories, and evaluate it accordingly"" vs ""as either subjective or objective by categorizing... its tone"" - -**Step 2: Randomly mutate the different parts** - -Mutated parts: -""Considering a given text"" -> ""Analyzing a provided passage"" -""determine the nature of the sentence or phrase"" -> ""assess the type of expression"" -""text"" -> ""entry"" -""sentence or phrase"" -> ""piece of writing"" -""into objective or subjective categories, and evaluate it accordingly"" -> ""based on their subjective or objective tone, classify them"" -""",0.55 -5,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -6,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -6,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -6,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly. -Prompt 2: Your goal is to evaluate every statement as either subjective or objective by categorizing a message according to its tone. - -Different parts: -""Considering a given text"" vs ""Your goal is to evaluate every statement"" -""determine the nature of the sentence or phrase"" vs ""categorizing a message"" -""text"" vs ""statement"" -""sentence or phrase"" vs ""message"" -""into objective or subjective categories, and evaluate it accordingly"" vs ""as either subjective or objective by categorizing... its tone"" - -**Step 2: Randomly mutate the different parts** - -Mutated parts: -""Considering a given text"" -> ""Analyzing a provided passage"" -""determine the nature of the sentence or phrase"" -> ""assess the type of expression"" -""text"" -> ""entry"" -""sentence or phrase"" -> ""piece of writing"" -""into objective or subjective categories, and evaluate it accordingly"" -> ""based on their subjective or objective tone, classify them"" -""",0.55 -6,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -7,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -7,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -7,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly. -Prompt 2: Your goal is to evaluate every statement as either subjective or objective by categorizing a message according to its tone. - -Different parts: -""Considering a given text"" vs ""Your goal is to evaluate every statement"" -""determine the nature of the sentence or phrase"" vs ""categorizing a message"" -""text"" vs ""statement"" -""sentence or phrase"" vs ""message"" -""into objective or subjective categories, and evaluate it accordingly"" vs ""as either subjective or objective by categorizing... its tone"" - -**Step 2: Randomly mutate the different parts** - -Mutated parts: -""Considering a given text"" -> ""Analyzing a provided passage"" -""determine the nature of the sentence or phrase"" -> ""assess the type of expression"" -""text"" -> ""entry"" -""sentence or phrase"" -> ""piece of writing"" -""into objective or subjective categories, and evaluate it accordingly"" -> ""based on their subjective or objective tone, classify them"" -""",0.55 -7,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -8,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -8,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -8,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly. -Prompt 2: Your goal is to evaluate every statement as either subjective or objective by categorizing a message according to its tone. - -Different parts: -""Considering a given text"" vs ""Your goal is to evaluate every statement"" -""determine the nature of the sentence or phrase"" vs ""categorizing a message"" -""text"" vs ""statement"" -""sentence or phrase"" vs ""message"" -""into objective or subjective categories, and evaluate it accordingly"" vs ""as either subjective or objective by categorizing... its tone"" - -**Step 2: Randomly mutate the different parts** - -Mutated parts: -""Considering a given text"" -> ""Analyzing a provided passage"" -""determine the nature of the sentence or phrase"" -> ""assess the type of expression"" -""text"" -> ""entry"" -""sentence or phrase"" -> ""piece of writing"" -""into objective or subjective categories, and evaluate it accordingly"" -> ""based on their subjective or objective tone, classify them"" -""",0.55 -8,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -9,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -9,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -9,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -9,"I'll follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: identify whether the given sentence was expressing an objective or a subjective opinion. -Prompt 2: You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. - -Different parts: -""identify whether the given sentence"" vs ""You need to determine the nature of the sentence"" -""was expressing an objective or a subjective opinion"" vs ""by categorizing it as having a subjective or objective tone"" -""opinion"" vs ""tone"" -""given sentence"" vs ""every message"" - -**Step 2: Randomly mutate the different parts** - -""identify whether the given sentence"" -> ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective'].",0.6 -9,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -10,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -10,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -10,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -10,"I'll follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: identify whether the given sentence was expressing an objective or a subjective opinion. -Prompt 2: You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. - -Different parts: -""identify whether the given sentence"" vs ""You need to determine the nature of the sentence"" -""was expressing an objective or a subjective opinion"" vs ""by categorizing it as having a subjective or objective tone"" -""opinion"" vs ""tone"" -""given sentence"" vs ""every message"" - -**Step 2: Randomly mutate the different parts** - -""identify whether the given sentence"" -> ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective'].",0.6 -10,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -11,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -11,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -11,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -11,"I'll follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: identify whether the given sentence was expressing an objective or a subjective opinion. -Prompt 2: You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. - -Different parts: -""identify whether the given sentence"" vs ""You need to determine the nature of the sentence"" -""was expressing an objective or a subjective opinion"" vs ""by categorizing it as having a subjective or objective tone"" -""opinion"" vs ""tone"" -""given sentence"" vs ""every message"" - -**Step 2: Randomly mutate the different parts** - -""identify whether the given sentence"" -> ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective'].",0.6 -11,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**3. Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.65 -12,"Analyze the provided text to decide whether it falls under subjective or objective category, considering the expressed opinion and its sentiment.",0.75 -12,"Considering a given text, determine the nature of the sentence or phrase into objective or subjective categories, and evaluate it accordingly.",0.65 -12,Determine the sentiment of a given text by examining a piece of writing and assess the tone as either subjective or objective.,0.7 -12,"I'll follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: identify whether the given sentence was expressing an objective or a subjective opinion. -Prompt 2: You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. - -Different parts: -""identify whether the given sentence"" vs ""You need to determine the nature of the sentence"" -""was expressing an objective or a subjective opinion"" vs ""by categorizing it as having a subjective or objective tone"" -""opinion"" vs ""tone"" -""given sentence"" vs ""every message"" - -**Step 2: Randomly mutate the different parts** - -""identify whether the given sentence"" -> ""recognize how the statement"" -""was expressing an objective or a subjective opinion"" -> ""holds an objective or subjective viewpoint"" -""opinion"" -> ""perspective"" -""given sentence"" -> ""provided messages"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective'].",0.6 -12,"You need to determine the nature of the sentence by categorizing it as having a subjective or objective tone, evaluating every message according to its tone. ""assess the provided texts"" -""determine whether they are subjective or objective"" -> ""classify as subjective or objective"" -""Please perform Subjectivity Classification task"" -> ""Complete a subjectivity analysis task"" -""Return label only without any other text"" -> ""Provide the classification result"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Your task is to classify the comment",0.65 -2,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -3,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -3,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -3,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -3,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -3,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -3,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -3,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -3,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -3,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -4,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -4,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -4,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -4,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -4,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -4,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -4,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -4,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -4,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -5,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -5,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -5,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -5,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -5,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -5,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -5,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -5,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -5,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -6,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -6,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -6,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -6,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -6,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -6,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -6,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -6,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -6,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -7,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -7,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -7,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -7,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -7,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -7,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -7,identify whether the given sentence was expressing an objective or a subjective opinion.,0.55 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -7,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -7,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -8,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -8,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -8,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -8,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -8,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -8,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -8,"Label every phrase into one of two categories: objective or subjective, by determining whether it expresses a subjective or an objective opinion.",0.6 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -8,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -8,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -9,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -9,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -9,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -9,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -9,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -9,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -9,"Label every phrase into one of two categories: objective or subjective, by determining whether it expresses a subjective or an objective opinion.",0.6 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -9,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -9,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -10,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -10,"Considering its content, identify the nature of a passage as expressing a subjective or objective opinion from its wording.",0.85 -10,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -10,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -10,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -10,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -10,"Label every phrase into one of two categories: objective or subjective, by determining whether it expresses a subjective or an objective opinion.",0.6 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -10,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -10,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -11,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -11,"Considering its content, identify the nature of a passage as expressing a subjective or objective opinion from its wording.",0.85 -11,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -11,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -11,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -11,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -11,"Undertake the task of categorizing expressions into either subjective or objective classifications by examining the provided phrase and determining whether it expresses a subjective or an objective opinion, then label as either subjective or objective.",0.85 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -11,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -11,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 -12,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.8 -12,"Considering its content, identify the nature of a passage as expressing a subjective or objective opinion from its wording.",0.85 -12,"Determine the category of a movie review based on a given text, subjective or objective.",0.7 -12,"You need to identify the provided text as either objective or subjective, by evaluating each sentence.",0.7 -12,"classify each sentence as either ""objective"" or ""subjective"".",0.65 -12,"Given segments of movie reviews, determine the classification of each phrase into one of the following categories: subjective or objective, assessing the context and tone accordingly.",0.75 -12,"Undertake the task of categorizing expressions into either subjective or objective classifications by examining the provided phrase and determining whether it expresses a subjective or an objective opinion, then label as either subjective or objective.",0.85 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Given a sentence, classify it as either subjective or objective category. -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: -""Given a sentence, classify it"" vs ""Please perform Subjectivity Classification task. Given the sentence, assign a label"" -""classify it as either subjective or objective category"" vs ""assign a label from ['subjective', 'objective']. Return label only without any other text"" - -**Step 2: Randomly mutate the different parts** - -""Given a sentence, classify it"" -> ""Examine the provided phrase and determine its"" -""classify it as either subjective or objective category"" -> ""categorize into either subjective or objective classifications"" - -""Please perform Subjectivity Classification task"" -> "" Undertake the task of categorizing expressions into"" -""assign a label from ['subjective', 'objective']"" -> ""label as either subjective or objective"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt",0.75 -12,Complete a subjectivity analysis task by assessing the provided texts and classify as subjective or objective. Provide the classification result.,0.75 -12,"Evaluate the provided text from a movie review and label as either subjective or objective in nature, assessing the statement and determining its category accordingly.",0.85 diff --git a/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 1a10fc1..0000000 --- a/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,1226 +0,0 @@ -step,prompt,score -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -1,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -1,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -1,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -1,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -1,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.45 -1,"Your task is to classify the comment ""subjective"" or ""objective"".",0.4 -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.4 -1,"Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear.",0.55 -1,Analyze the declaration and assign a label of 'subjective' or 'objective' based on the sentiment expressed.,0.4 -2,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -2,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -2,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -2,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -2,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -2,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.45 -2,"Your task is to classify the comment ""subjective"" or ""objective"".",0.4 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.4 -2,"Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear.",0.55 -2,Analyze the declaration and assign a label of 'subjective' or 'objective' based on the sentiment expressed.,0.4 -3,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -3,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -3,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -3,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -3,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -3,"Examine the statement and identify the emotions conveyed in the text, then classify it as 'neutral', 'bias', based on the presence of subjective or objective tone. ""Analyze the given statement"" -""topic-neutral or opinion-based"" -> ""either objective or subjective"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -""perceptual or factual"" -> ""either concrete or abstract"" -""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed with",0.5 -3,"Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear.",0.55 -3,Analyze the declaration and assign a label of 'subjective' or 'objective' based on the sentiment expressed.,0.4 -4,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -4,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -4,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -4,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -4,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: -""Assess the given phrase"" -> ""Analyze the statement"" -""topic-neutral or opinion-based"" -> ""either objective or subjective"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -""perceptual or factual"" -> ""either concrete or abstract"" -""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed",0.55 -4,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -4,"Here are the results of following the instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the given statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and",0.55 -4,"Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear.",0.55 -4,Analyze the declaration and assign a label of 'subjective' or 'objective' based on the sentiment expressed.,0.4 -5,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -5,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -5,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -5,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -5,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: -""Assess the given phrase"" -> ""Analyze the statement"" -""topic-neutral or opinion-based"" -> ""either objective or subjective"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -""perceptual or factual"" -> ""either concrete or abstract"" -""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed",0.55 -5,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -5,"Here are the results of following the instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the given statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and",0.55 -5,"Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear.",0.55 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -6,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -6,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -6,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -6,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: -""Assess the given phrase"" -> ""Analyze the statement"" -""topic-neutral or opinion-based"" -> ""either objective or subjective"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -""perceptual or factual"" -> ""either concrete or abstract"" -""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed",0.55 -6,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -6,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -7,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -7,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -7,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -7,Task is to identify the sentence and determine whether it is either objective or subjective.,0.6 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: -""Assess the given phrase"" -> ""Analyze the statement"" -""topic-neutral or opinion-based"" -> ""either objective or subjective"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -""perceptual or factual"" -> ""either concrete or abstract"" -""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed",0.55 -7,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -7,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -8,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -8,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -8,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -8,"Evaluate the evaluation and categorize the nature of the information in the given sentence as either objective or impression, considering whether it falls under",0.65 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: -""Assess the given phrase"" -> ""Analyze the statement"" -""topic-neutral or opinion-based"" -> ""either objective or subjective"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -""perceptual or factual"" -> ""either concrete or abstract"" -""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt bracketed",0.55 -8,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -8,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -9,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -9,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -9,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -9,"Evaluate the evaluation and categorize the nature of the information in the given sentence as either objective or impression, considering whether it falls under",0.65 -9,"I'd be happy to help you generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt: - -Prompt",0.6 -9,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -9,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -10,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -10,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -10,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -10,"Evaluate the evaluation and categorize the nature of the information in the given sentence as either objective or impression, considering whether it falls under",0.65 -10,"I'd be happy to help you generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt: - -Prompt",0.6 -10,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -10,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -11,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -11,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -11,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -11,"Evaluate the evaluation and categorize the nature of the information in the given sentence as either objective or impression, considering whether it falls under",0.65 -11,"I'd be happy to help you generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt: - -Prompt",0.6 -11,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -11,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Evaluate the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt",0.6 -12,"Examine claims and assess whether they express a subjective or objective opinion, given the information provided.",0.65 -12,"Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.",0.7 -12,Consider the assessment and determine the stance of each sentence as either subjective or objective.,0.55 -12,"Evaluate the evaluation and categorize the nature of the information in the given sentence as either objective or impression, considering whether it falls under",0.65 -12,"I'd be happy to help you generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following Prompt: - -Prompt",0.6 -12,"I'm happy to help! Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with the following Prompt",0.55 -12,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the statement"" -* ""topic-neutral or opinion-based"" -> ""either objective or subjective"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying tone and message"" - -* ""perceptual or factual"" -> ""either concrete or abstract"" -* ""while considering the context in which they appear"" -> ""by examining the broader implications"" - -3. Crossover the different parts with the following",0.6 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -* ""Assess the given phrase"" vs ""Determine the nature of the assertion"" -* ""topic-neutral or opinion-based"" vs ""perceptual or factual"" -* ""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -* ""Assess the given phrase"" -> ""Analyze the provided language"" -* ""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -* ""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -* ""perceptual or factual"" -> ""concrete or abstract information"" -* ""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with",0.6 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. -Prompt 2: Determine the nature of the assertion as either perceptual or factual for sentences or phrases, while considering the context in which they appear. - -Different parts: - -""Assess the given phrase"" vs ""Determine the nature of the assertion"" -""topic-neutral or opinion-based"" vs ""perceptual or factual"" -""determining whether it falls under attitude or reality"" vs ""while considering the context in which they appear"" - -2. Randomly mutate the different parts: - -""Assess the given phrase"" -> ""Analyze the provided language"" -""topic-neutral or opinion-based"" -> ""objective or emotive tone"" -""determining whether it falls under attitude or reality"" -> ""examining the underlying sentiment"" - -""perceptual or factual"" -> ""concrete or abstract information"" -""while considering the context in which they appear"" -> ""in the light of the broader narrative"" - -3. Crossover the different parts with Prompt 3 to generate a final prompt: - -Prompt",0.5 diff --git a/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 46561f2..0000000 --- a/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,319 +0,0 @@ -step,prompt,score -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -1,evaluate each statement as either subjective or objective.,0.7 -1,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -1,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.55 -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -1,evaluate each sentence as either objective or subjective.,0.45 -1,"As a sentiment analyzer, determine if a sentence about a movie has an objective or subjective focus and classify it accordingly.",0.5 -1,Analyze the expression to identify whether a text passage is a subjective or objective,0.5 -1,Classify a text extract into either a subjective or objective type or classification by examining the document and considering,0.4 -2,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -2,evaluate each statement as either subjective or objective.,0.7 -2,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -2,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -2,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -2,Analyze the passage and identify as being either subjective or objective in nature.,0.6 -2,"As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie.",0.6 -2,Analyze the expression to identify whether a text passage is a subjective or objective,0.5 -2,Classify a text extract into either a subjective or objective type or classification by examining the document and considering,0.4 -3,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -3,evaluate each statement as either subjective or objective.,0.7 -3,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -3,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -3,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -3,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -3,Analyze the passage and identify as being either subjective or objective in nature.,0.6 -3,"As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie.",0.6 -3,Analyze the expression to identify whether a text passage is a subjective or objective,0.5 -3,"Examine the text to determine the nature of the passage and categorize the tone as objective or subjective, considering the overall tone and",0.5 -4,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -4,evaluate each statement as either subjective or objective.,0.7 -4,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -4,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -4,"Given a statement, classify it as expressing a subjective or objective opinion.",0.55 -4,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -4,Analyze the passage and identify as being either subjective or objective in nature.,0.6 -4,"As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie.",0.6 -4,Analyze the expression to identify whether a text passage is a subjective or objective,0.5 -4,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -5,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -5,evaluate each statement as either subjective or objective.,0.7 -5,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -5,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -5,"Classify the text according to its inherent perspective or examine the text to determine its orientation, and categorize it as either expressing a subjective or objective opinion.",0.6 -5,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -5,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -5,"As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie.",0.6 -5,"As you analyze the review, identify the type of review in terms of its tone, determining whether it is subjective or objective.",0.6 -5,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -6,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -6,evaluate each statement as either subjective or objective.,0.7 -6,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -6,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -6,"Classify the text according to its inherent perspective or examine the text to determine its orientation, and categorize it as either expressing a subjective or objective opinion.",0.6 -6,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -6,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -6,"As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie.",0.6 -6,"As you analyze the review, identify the type of review in terms of its tone, determining whether it is subjective or objective.",0.6 -6,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -7,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -7,evaluate each statement as either subjective or objective.,0.7 -7,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -7,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -7,"Classify the text according to its inherent perspective or examine the text to determine its orientation, and categorize it as either expressing a subjective or objective opinion.",0.6 -7,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -7,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -7,"As a sentiment analyzer, analyze the text about a movie and categorize it as expressing a subjective or objective opinion.",0.65 -7,"As a sentiment analyzer, examine the text and identify whether the opinion is objective or subjective about a movie, while analyzing the review and considering its tone.",0.65 -7,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -8,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -8,evaluate each statement as either subjective or objective.,0.7 -8,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -8,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -8,"Classify the text according to its inherent perspective or examine the text to determine its orientation, and categorize it as either expressing a subjective or objective opinion.",0.6 -8,"Determine the category of a movie review based on a given text, subjective or objective.",0.55 -8,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -8,"As a sentiment analyzer, analyze the text about a movie and categorize it as expressing a subjective or objective opinion.",0.65 -8,"As a sentiment analyzer, examine the text and identify whether the opinion is objective or subjective about a movie, while analyzing the review and considering its tone.",0.65 -8,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -9,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -9,evaluate each statement as either subjective or objective.,0.7 -9,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -9,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -9,"Classify the text according to its inherent perspective or examine the text to determine its orientation, and categorize it as either expressing a subjective or objective opinion.",0.6 -9,"As a sentiment expert, inspect the passage to classify the sentiment as objective or subjective regarding the film, taking into account the movie critique and its nuances.",0.75 -9,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -9,"As a sentiment analyzer, analyze the text about a movie and categorize it as expressing a subjective or objective opinion.",0.65 -9,"As a sentiment analyzer, examine the text and identify whether the opinion is objective or subjective about a movie, while analyzing the review and considering its tone.",0.65 -9,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -10,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -10,evaluate each statement as either subjective or objective.,0.7 -10,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -10,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -10,"Classify the text according to its inherent perspective or examine the text to determine its orientation, and categorize it as either expressing a subjective or objective opinion.",0.6 -10,"As a sentiment expert, inspect the passage to classify the sentiment as objective or subjective regarding the film, taking into account the movie critique and its nuances.",0.75 -10,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -10,"As a sentiment analyzer, analyze the text about a movie and categorize it as expressing a subjective or objective opinion.",0.65 -10,"As a sentiment analyzer, examine the text and identify whether the opinion is objective or subjective about a movie, while analyzing the review and considering its tone.",0.65 -10,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -11,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -11,evaluate each statement as either subjective or objective.,0.7 -11,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -11,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -11,"Investigate the provided phrases and identify the tone and inclination, with the goal of classifying the text according to its inherent perspective or examining its orientation to determine whether it expresses a subjective or",0.65 -11,"As a sentiment expert, inspect the passage to classify the sentiment as objective or subjective regarding the film, taking into account the movie critique and its nuances.",0.75 -11,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -11,"As a sentiment analyzer, analyze the text about a movie and categorize it as expressing a subjective or objective opinion.",0.65 -11,"As a sentiment analyzer, examine the text and identify whether the opinion is objective or subjective about a movie, while analyzing the review and considering its tone.",0.65 -11,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 -12,identify whether the given sentence was expressing an objective or a subjective opinion.,0.75 -12,evaluate each statement as either subjective or objective.,0.7 -12,evaluate the given sentences and determine whether they are subjective or objective.,0.65 -12,"As a sentiment classifier, analyze the statement to determine whether it is objective or subjective.",0.7 -12,"Investigate the provided phrases and identify the tone and inclination, with the goal of classifying the text according to its inherent perspective or examining its orientation to determine whether it expresses a subjective or",0.65 -12,"As a sentiment expert, inspect the passage to classify the sentiment as objective or subjective regarding the film, taking into account the movie critique and its nuances.",0.75 -12,"Assess the passage to determine its type, classifying a series of statements as having a clear perspective or neutral point of view, in order to determine whether it is subjective or objective.",0.65 -12,"As a sentiment analyzer, analyze the text about a movie and categorize it as expressing a subjective or objective opinion.",0.65 -12,"As a sentiment analyzer, examine the text and identify whether the opinion is objective or subjective about a movie, while analyzing the review and considering its tone.",0.65 -12,"Here are the steps to generate a better prompt: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: As a sentiment analyzer, analyze the provided text or identify the sentiment, and categorize it as expressing a subjective or objective opinion about a movie. -Prompt 2: evaluate each statement as either subjective or objective. - -Different parts: - -* ""As a sentiment analyzer"" vs ""evaluate each statement"" -* ""analyze the provided text"" vs ""as either subjective or objective"" -* ""identify the sentiment"" vs (no corresponding part in Prompt 2) -* ""categorize it as expressing a subjective or objective opinion about a movie"" vs ""as either subjective or objective"" - -2. Randomly mutate the different parts: - -* ""As a sentiment analyzer"" -> ""Determine the tone of the passage"" -* ""analyze the provided text"" -> ""Examine the statement"" -* ""identify the sentiment"" -> ""Determine the emotional tone"" -* ""categorize it as expressing a subjective or objective opinion about a movie"" -> ""Classify the tone as"" -* (no corresponding part in Prompt 2) -> ""and distinguish between personal and factual expressions"" - -3. Crossover the different parts with the following Prompt 3",0.55 diff --git a/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 801c87d..0000000 --- a/logs/experiment/subj_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,271 +0,0 @@ -step,prompt,score -1,"Your task is to classify the comment ""subjective"" or ""objective"".",0.7 -1,"Given a statement, classify it as expressing a subjective or objective opinion.",0.7 -1,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -1,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: classify each sentence as either ""objective"" or ""subjective"". -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: - -"" classify each sentence as either ""objective"" or ""subjective"" "" vs "" Please perform Subjectivity Classification task "" -""objective"" or ""subjective"" vs ['subjective', 'objective'] - -Let me know when you're ready for me to proceed with random mutagenesis and crossover!",0.7 -1,evaluate each sentence as either objective or subjective.,0.65 -1,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -1,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -1,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.6 -1,"classify each sentence as either ""objective"" or ""subjective"".",0.5 -1,"Given a sentence, classify it as either subjective or objective category.",0.5 -2,"Your task is to classify the comment ""subjective"" or ""objective"".",0.7 -2,Make a judgment about the utterance and categorize it as either expressing a subjective or objective opinion.,0.75 -2,identify whether the given sentence was expressing an objective or a subjective opinion.,0.65 -2,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: classify each sentence as either ""objective"" or ""subjective"". -Prompt 2: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. - -Different parts: - -"" classify each sentence as either ""objective"" or ""subjective"" "" vs "" Please perform Subjectivity Classification task "" -""objective"" or ""subjective"" vs ['subjective', 'objective'] - -Let me know when you're ready for me to proceed with random mutagenesis and crossover!",0.7 -2,evaluate each sentence as either objective or subjective.,0.65 -2,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -2,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -2,"Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text.",0.6 -2,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. -Prompt 2: Your task is to classify the comment ""subjective"" or ""objective"". - -Different parts: - -""Please perform"" vs ""Your task is to"" -""Classification task"" vs ""classify the comment"" -""Given the sentence, assign a label from ['subjective', 'objective']"" vs ""classify the comment 'subjective' or 'objective'"" - -""Return label only without any other text"" vs not present in Prompt 2 - -2. Randomly mutate the different parts: - -""Please perform"" -> ""The goal is to"" -""Classification task"" -> ""sentiment detection"" -""Given the sentence, assign a label from ['subjective', 'objective']"" -> ""determine the sentiment"" -""Return label only without any other text"" -> not mutated - -Commentated phrases in movie reviews classify them as either the sentiment detection task decides the new category, and then find the sign to decide the correct sentiment. - -3. Crossover the different parts with the",0.55 -2,"Given a sentence, classify it as either subjective or objective category.",0.5 -3,The goal is to classify the text as either subjective or objective classify them as subjective or objective.,0.8 -3,Make a judgment about the utterance and categorize it as either expressing a subjective or objective opinion.,0.75 -3,"Determine the classification of the given text into one of the following categories: facts or opinions, while identifying whether the sentence expresses an objective or subjective opinion.",0.8 -3,"Determine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction.",0.85 -3,evaluate each sentence as either objective or subjective.,0.65 -3,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -3,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -3,"As a task, classify text as either subjective in either subjective or objective terms, given a sentence, while using subjectivity classification and returning a label from ['subjective', 'objective'].",0.7 -3,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. -Prompt 2: Your task is to classify the comment ""subjective"" or ""objective"". - -Different parts: - -""Please perform"" vs ""Your task is to"" -""Classification task"" vs ""classify the comment"" -""Given the sentence, assign a label from ['subjective', 'objective']"" vs ""classify the comment 'subjective' or 'objective'"" - -""Return label only without any other text"" vs not present in Prompt 2 - -2. Randomly mutate the different parts: - -""Please perform"" -> ""The goal is to"" -""Classification task"" -> ""sentiment detection"" -""Given the sentence, assign a label from ['subjective', 'objective']"" -> ""determine the sentiment"" -""Return label only without any other text"" -> not mutated - -Commentated phrases in movie reviews classify them as either the sentiment detection task decides the new category, and then find the sign to decide the correct sentiment. - -3. Crossover the different parts with the",0.55 -3,"Given a sentence, classify it as either subjective or objective category.",0.5 -4,The goal is to classify the text as either subjective or objective classify them as subjective or objective.,0.8 -4,Make a judgment about the utterance and categorize it as either expressing a subjective or objective opinion.,0.75 -4,"Determine the classification of the given text into one of the following categories: facts or opinions, while identifying whether the sentence expresses an objective or subjective opinion.",0.8 -4,"Determine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction.",0.85 -4,evaluate each sentence as either objective or subjective.,0.65 -4,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -4,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -4,"As a task, classify text as either subjective in either subjective or objective terms, given a sentence, while using subjectivity classification and returning a label from ['subjective', 'objective'].",0.7 -4,"Let's follow the instructions step-by-step. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. -Prompt 2: Your task is to classify the comment ""subjective"" or ""objective"". - -Different parts: - -""Please perform"" vs ""Your task is to"" -""Classification task"" vs ""classify the comment"" -""Given the sentence, assign a label from ['subjective', 'objective']"" vs ""classify the comment 'subjective' or 'objective'"" - -""Return label only without any other text"" vs not present in Prompt 2 - -2. Randomly mutate the different parts: - -""Please perform"" -> ""The goal is to"" -""Classification task"" -> ""sentiment detection"" -""Given the sentence, assign a label from ['subjective', 'objective']"" -> ""determine the sentiment"" -""Return label only without any other text"" -> not mutated - -Commentated phrases in movie reviews classify them as either the sentiment detection task decides the new category, and then find the sign to decide the correct sentiment. - -3. Crossover the different parts with the",0.55 -4,"Given a sentence, classify it as either subjective or objective category.",0.5 -5,The goal is to classify the text as either subjective or objective classify them as subjective or objective.,0.8 -5,Make a judgment about the utterance and categorize it as either expressing a subjective or objective opinion.,0.75 -5,"Determine the classification of the given text into one of the following categories: facts or opinions, while identifying whether the sentence expresses an objective or subjective opinion.",0.8 -5,"Determine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction.",0.85 -5,evaluate each sentence as either objective or subjective.,0.65 -5,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -5,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -5,"As a task, classify text as either subjective in either subjective or objective terms, given a sentence, while using subjectivity classification and returning a label from ['subjective', 'objective'].",0.7 -5,"I'd be happy to help you generate a better prompt by following the instructions step-by-step! - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. -Prompt 2: Your task is to classify the comment ""subjective"" or ""objective"". - -Different parts: - -* ""Please perform"" vs ""Your task is to"" -* ""Classification task"" vs ""classify the comment"" -* ""Given the sentence, assign a label from ['subjective', 'objective']"" vs ""classify the comment 'subjective' or 'objective'"" -* ""Return label only without any other text"" vs not present in Prompt 2 - -2. Randomly mutate the different parts: - -* ""Please perform"" -> ""The objective is to"" -* ""Classification task"" -> ""judgment task"" -* ""Given the sentence, assign a label from ['subjective', 'objective']"" -> ""determine the sentiment based on the sentence"" -* ""Return label only without any other text"" -> not mutated - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed",0.6 -5,"Given a sentence, classify it as either subjective or objective category.",0.5 -6,The goal is to classify the text as either subjective or objective classify them as subjective or objective.,0.8 -6,Make a judgment about the utterance and categorize it as either expressing a subjective or objective opinion.,0.75 -6,"Determine the classification of the given text into one of the following categories: facts or opinions, while identifying whether the sentence expresses an objective or subjective opinion.",0.8 -6,"Determine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction.",0.85 -6,evaluate each sentence as either objective or subjective.,0.65 -6,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -6,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -6,"As a task, classify text as either subjective in either subjective or objective terms, given a sentence, while using subjectivity classification and returning a label from ['subjective', 'objective'].",0.7 -6,"I'd be happy to help you generate a better prompt by following the instructions step-by-step! - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Please perform Subjectivity Classification task. Given the sentence, assign a label from ['subjective', 'objective']. Return label only without any other text. -Prompt 2: Your task is to classify the comment ""subjective"" or ""objective"". - -Different parts: - -* ""Please perform"" vs ""Your task is to"" -* ""Classification task"" vs ""classify the comment"" -* ""Given the sentence, assign a label from ['subjective', 'objective']"" vs ""classify the comment 'subjective' or 'objective'"" -* ""Return label only without any other text"" vs not present in Prompt 2 - -2. Randomly mutate the different parts: - -* ""Please perform"" -> ""The objective is to"" -* ""Classification task"" -> ""judgment task"" -* ""Given the sentence, assign a label from ['subjective', 'objective']"" -> ""determine the sentiment based on the sentence"" -* ""Return label only without any other text"" -> not mutated - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracketed",0.6 -6,"The objective is to perform a judgment task by determining the sentiment based on a sentence and returning a label from ['subjective', 'objective'] as",0.55 -7,The goal is to classify the text as either subjective or objective classify them as subjective or objective.,0.8 -7,Make a judgment about the utterance and categorize it as either expressing a subjective or objective opinion.,0.75 -7,"Determine the classification of the given text into one of the following categories: facts or opinions, while identifying whether the sentence expresses an objective or subjective opinion.",0.8 -7,"Determine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction.",0.85 -7,evaluate each sentence as either objective or subjective.,0.65 -7,"Determine the category of a movie review based on a given text, subjective or objective.",0.65 -7,"determine the categorization of the given statements as either objective or subjective, considering each sentence's semantic meaning.",0.65 -7,"As a task, classify text as either subjective in either subjective or objective terms, given a sentence, while using subjectivity classification and returning a label from ['subjective', 'objective'].",0.7 -7,"Here is the final prompt: - - ""Classify the sentence according to its type"" -""Given the question"" -> ""analyze the sentence"" -""assign a label from"" -> ""choose a category from the options"" -""Return label only without any other text"" -> ""provide the classification",0.25 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -3,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -3,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -3,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -3,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number.",0.45 -3,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.35 -3,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -3,"Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.4 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -4,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -4,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -4,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -4,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number.",0.45 -4,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.35 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number, -Prompt 2: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. - -Different parts: -""Please categorize this sentence into one of the listed categories"" vs ""Your task is to choose a type of the question"" -""this sentence"" vs ""the question"" -""into one of the listed categories"" vs ""from"" - -**Step 2: Randomly mutate the different parts:** - -""Please categorize this sentence into one of the listed categories"" -> ""Identify the class of the given sentence among the following options"" -""this sentence"" -> ""the provided sentence"" -""into one of the listed categories"" -> ""according to the provided list"" - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt:** - -Prompt 3: Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number",0.3 -4,"Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.4 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -5,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -5,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -5,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -5,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -5,"Let's go through the steps to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: -""Classify this sentence"" vs ""Please perform Question Classification task. Given the question, assign a label"" -""based on the provided categories"" vs ""from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -""categories"" vs ""label"" -""sentence"" vs ""question"" - -**Step 2: Randomly mutate the different parts** - -""Classify this sentence"" -> ""Categorize the given text"" -""Please perform Question Classification task. Given the question, assign a label"" -> ""Your task involves labeling a question"" -""based on the provided categories"" -> ""according to the specified classes"" -""categories"" -> ""classes"" -""sentence"" -> ""query"" -""question"" -> ""query""",0.45 -5,"Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number.",0.45 -5,"Let's follow the instructions. - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification. -Prompt 2: Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Classify the sentence according to its type by analyzing the sentence"" vs ""Identify the category that corresponds to the given sentence"" -""and choose a category from the options"" vs ""and categorize it according to one of the following"" -""and provide the classification"" -> (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: - -""Classify the sentence according to its type by analyzing the sentence"" -> ""Determine the classification of the given sentence"" -""and choose a category from the options"" -> ""from the available categories"" -""and provide the classification"" -> ""with an explanation"" (added a new mutation) -""Identify the category that corresponds to the given sentence"" -> ""Recognize the category",0.45 -5,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -5,"Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.4 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -6,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -6,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -6,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -6,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -6,"Let's go through the steps to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: -""Classify this sentence"" vs ""Please perform Question Classification task. Given the question, assign a label"" -""based on the provided categories"" vs ""from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -""categories"" vs ""label"" -""sentence"" vs ""question"" - -**Step 2: Randomly mutate the different parts** - -""Classify this sentence"" -> ""Categorize the given text"" -""Please perform Question Classification task. Given the question, assign a label"" -> ""Your task involves labeling a question"" -""based on the provided categories"" -> ""according to the specified classes"" -""categories"" -> ""classes"" -""sentence"" -> ""query"" -""question"" -> ""query""",0.45 -6,"Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number.",0.45 -6,"Let's follow the instructions. - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification. -Prompt 2: Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Classify the sentence according to its type by analyzing the sentence"" vs ""Identify the category that corresponds to the given sentence"" -""and choose a category from the options"" vs ""and categorize it according to one of the following"" -""and provide the classification"" -> (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: - -""Classify the sentence according to its type by analyzing the sentence"" -> ""Determine the classification of the given sentence"" -""and choose a category from the options"" -> ""from the available categories"" -""and provide the classification"" -> ""with an explanation"" (added a new mutation) -""Identify the category that corresponds to the given sentence"" -> ""Recognize the category",0.45 -6,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -6,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -7,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -7,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -7,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -7,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -7,"Your task involves labeling a query according to the specified classes from the following options: Description, Entity, Expression, Human, Location, and Number. Categorize the given text by determining the correct type.",0.55 -7,"Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number.",0.45 -7,"Let's follow the instructions. - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification. -Prompt 2: Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Classify the sentence according to its type by analyzing the sentence"" vs ""Identify the category that corresponds to the given sentence"" -""and choose a category from the options"" vs ""and categorize it according to one of the following"" -""and provide the classification"" -> (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: - -""Classify the sentence according to its type by analyzing the sentence"" -> ""Determine the classification of the given sentence"" -""and choose a category from the options"" -> ""from the available categories"" -""and provide the classification"" -> ""with an explanation"" (added a new mutation) -""Identify the category that corresponds to the given sentence"" -> ""Recognize the category",0.45 -7,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -7,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -8,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -8,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -8,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -8,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -8,"Your task involves labeling a query according to the specified classes from the following options: Description, Entity, Expression, Human, Location, and Number. Categorize the given text by determining the correct type.",0.55 -8,"Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number.",0.45 -8,"Let's follow the instructions. - -1. Identifying the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Classify the sentence according to its type by analyzing the sentence and choose a category from the options: Description, Entity, Expression, Human, Location, or Number, and provide the classification. -Prompt 2: Identify the category that corresponds to the given sentence, and categorize it according to one of the following: Description, Entity, Expression, Human, Location, or Number. - -Different parts: -""Classify the sentence according to its type by analyzing the sentence"" vs ""Identify the category that corresponds to the given sentence"" -""and choose a category from the options"" vs ""and categorize it according to one of the following"" -""and provide the classification"" -> (no equivalent part in Prompt 2) - -2. Randomly mutate the different parts: - -""Classify the sentence according to its type by analyzing the sentence"" -> ""Determine the classification of the given sentence"" -""and choose a category from the options"" -> ""from the available categories"" -""and provide the classification"" -> ""with an explanation"" (added a new mutation) -""Identify the category that corresponds to the given sentence"" -> ""Recognize the category",0.45 -8,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -8,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -9,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -9,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -9,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -9,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -9,"Your task involves labeling a query according to the specified classes from the following options: Description, Entity, Expression, Human, Location, and Number. Categorize the given text by determining the correct type.",0.55 -9,"Examine the provided sentence and determine its classification based on the following categories: Description, Entity, Expression, Human, Location, or Number, where you identify the category that corresponds to the given sentence.",0.55 -9,"Recognize the category of the given sentence, determine the classification of the sentence, and categorize it from the available categories: Description, Entity, Expression, Human, Location, or Number, with an explanation.",0.5 -9,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -9,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -10,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -10,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -10,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -10,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -10,"Your task involves labeling a query according to the specified classes from the following options: Description, Entity, Expression, Human, Location, and Number. Categorize the given text by determining the correct type.",0.55 -10,"Examine the provided sentence and determine its classification based on the following categories: Description, Entity, Expression, Human, Location, or Number, where you identify the category that corresponds to the given sentence.",0.55 -10,"Recognize the category of the given sentence, determine the classification of the sentence, and categorize it from the available categories: Description, Entity, Expression, Human, Location, or Number, with an explanation.",0.5 -10,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -10,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -11,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -11,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -11,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -11,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -11,"Your task involves labeling a query according to the specified classes from the following options: Description, Entity, Expression, Human, Location, and Number. Categorize the given text by determining the correct type.",0.55 -11,"Examine the provided sentence and determine its classification based on the following categories: Description, Entity, Expression, Human, Location, or Number, where you identify the category that corresponds to the given sentence.",0.55 -11,"Recognize the category of the given sentence, determine the classification of the sentence, and categorize it from the available categories: Description, Entity, Expression, Human, Location, or Number, with an explanation.",0.5 -11,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -11,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.95 -12,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -12,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.75 -12,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.6 -12,"Please categorize this sentence into one of the listed categories: Description, Entity, Expression, Human, Location, or Number,",0.6 -12,"Your task involves labeling a query according to the specified classes from the following options: Description, Entity, Expression, Human, Location, and Number. Categorize the given text by determining the correct type.",0.55 -12,"Examine the provided sentence and determine its classification based on the following categories: Description, Entity, Expression, Human, Location, or Number, where you identify the category that corresponds to the given sentence.",0.55 -12,"Recognize the category of the given sentence, determine the classification of the sentence, and categorize it from the available categories: Description, Entity, Expression, Human, Location, or Number, with an explanation.",0.5 -12,"Identify the class of the provided sentence among the following options according to the provided list: Description, Entity, Expression, Human, Location, or Number.",0.35 -12,"By analyzing the given query, categorize it according to its type by choosing a category from the specified classes: Description, Entity, Expression, Human, Location, or Number, and provide the classification.",0.6 diff --git a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv b/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv deleted file mode 100644 index 0b7eccd..0000000 --- a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_47.csv +++ /dev/null @@ -1,121 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -1,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -1,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -1,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -1,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -1,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -1,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Identify the suitable label for this sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -1,"Select the correct label to the given text from the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -2,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -2,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -2,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -2,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Identify the suitable label for this sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"Select the correct label to the given text from the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -3,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -3,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -3,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -3,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -3,"Identify the suitable label for this sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"Select the correct label to the given text from the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -4,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -4,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -4,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -4,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -4,"Identify the suitable label for this sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"Select the correct label to the given text from the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -5,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -5,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -5,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -5,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -5,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -5,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -5,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -5,"Identify the suitable label for this sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -5,"Select the correct label to the given text from the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -6,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -6,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -6,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -6,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -6,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -6,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -6,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -6,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -6,"Select the correct label to the given text from the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -7,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -7,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -7,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -7,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -7,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -7,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -7,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -7,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -7,"Classify the provided input into its appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number, by selecting the correct type.",0.55 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -8,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -8,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -8,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -8,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -8,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -8,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -8,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -8,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -8,"Classify the provided input into its appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number, by selecting the correct type.",0.55 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -9,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -9,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -9,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -9,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -9,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -9,"Identify the category of the phrase by assigning it to one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.4 -9,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -9,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -9,"Classify the provided input into its appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number, by selecting the correct type.",0.55 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -10,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -10,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -10,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -10,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -10,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -10,"Classify the attribute of the posed inquiry by identifying the category it belongs to, which can be one of the following options: Description, Entity",0.6 -10,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -10,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -10,"Classify the provided input into its appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number, by selecting the correct type.",0.55 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -11,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -11,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -11,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -11,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -11,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -11,"Classify the attribute of the posed inquiry by identifying the category it belongs to, which can be one of the following options: Description, Entity",0.6 -11,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -11,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -11,"Classify the provided input into its appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number, by selecting the correct type.",0.55 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.7 -12,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.6 -12,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.6 -12,"Classify the category of the provided sentence by selecting among Description, Entity, Expression, Human, Location, or Number that best corresponds",0.55 -12,"You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.",0.75 -12,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -12,"Classify the attribute of the posed inquiry by identifying the category it belongs to, which can be one of the following options: Description, Entity",0.6 -12,"Determine the classification of the sentence fragment by assigning it to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.6 -12,"You need to classify the provided sentence by recognizing the most fitting class from the following options: Description, Entity, Expression, Human, Location, or Number.",0.55 -12,"Classify the provided input into its appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number, by selecting the correct type.",0.55 diff --git a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv b/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv deleted file mode 100644 index f885df0..0000000 --- a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_69.csv +++ /dev/null @@ -1,180 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.75 -1,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -1,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -1,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.5 -1,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -1,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -1,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Please perform Question Classification task. Given the question"" vs ""Classify this sentence"" -""assign a label from"" vs ""based on the provided categories"" -""Return label only without any other text"" vs (no equivalent part in Prompt 2) - -**Step 2: Randomly mutate the different parts** - -""Please perform Question Classification task. Given the question"" -> ""Your goal is to analyze the query"" -""assign a label from"" -> ""categorize into one of the following"" -""Return label only without any other text"" -> (delete this part, as it's not essential) - -**Step 3: Crossover the different parts with Prompt 3 and generate a final prompt** - -Prompt 3: Using the following categories",0.3 -1,"Identify the appropriate category for the sentence by categorizing it according to the following labels: Description, Entity, Expression, Human, Location, or Number.",0.3 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.75 -2,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -2,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -2,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -2,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -2,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -2,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -2,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -2,"Your goal is to analyze the query and categorize into one of the following: Description, Entity, Expression, Human, Location, or Number.",0.55 -2,"Here are the steps to generate a better prompt: - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: -""pick the category"" vs ""perform Question Classification task"" -""that matches this sentence"" vs ""Given the question"" -""Description, Entity, Expression, Human, Location, or Number"" vs ""from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -""Return label only without any other text"" (only exists in Prompt 2) - -**2. Randomly mutate the different parts:** - -""pick the category"" -> ""select the classification"" -""that matches this sentence"" -> ""corresponding to the provided text"" -""perform Question Classification task"" -> ""carry out the classification process"" -""Given the question"" -> ""based on the input sentence"" -""Description, Entity, Expression, Human, Location, or Number""",0.4 -3,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -3,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -3,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -3,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -3,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -3,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -3,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -3,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -3,"Your goal is to analyze the query and categorize into one of the following: Description, Entity, Expression, Human, Location, or Number.",0.55 -3,"Here are the steps to generate a better prompt: - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: -""pick the category"" vs ""perform Question Classification task"" -""that matches this sentence"" vs ""Given the question"" -""Description, Entity, Expression, Human, Location, or Number"" vs ""from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -""Return label only without any other text"" (only exists in Prompt 2) - -**2. Randomly mutate the different parts:** - -""pick the category"" -> ""select the classification"" -""that matches this sentence"" -> ""corresponding to the provided text"" -""perform Question Classification task"" -> ""carry out the classification process"" -""Given the question"" -> ""based on the input sentence"" -""Description, Entity, Expression, Human, Location, or Number""",0.4 -4,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -4,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -4,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -4,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -4,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -4,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -4,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -4,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -4,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -4,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -5,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -5,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -5,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -5,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -5,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -5,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -5,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -5,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -5,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -5,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -6,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -6,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -6,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -6,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -6,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -6,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -6,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -6,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -6,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -6,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -7,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -7,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -7,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -7,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -7,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -7,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -7,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -7,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -7,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -7,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -8,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -8,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -8,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -8,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -8,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -8,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -8,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -8,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -8,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -8,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -9,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -9,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -9,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -9,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -9,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -9,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -9,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -9,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -9,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -9,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -10,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -10,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -10,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -10,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -10,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -10,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -10,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -10,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -10,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -10,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -11,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -11,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -11,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -11,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -11,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -11,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -11,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -11,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -11,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -11,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 -12,"As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.",0.8 -12,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.7 -12,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.65 -12,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.6 -12,"Your goal is to analyze the query and identify the category that corresponds to it, categorize into one of the following: Description, Entity, Expression, Human, Location, or Number, according to the following types.",0.65 -12,"Analyze the given sentence and determine the appropriate category it falls under: Description, Entity, Expression, Human, Location, or Number.",0.55 -12,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -12,"Identify the correct category for the sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -12,"Your goal is to analyze the query and choose the appropriate label from the following categories: Description, Entity, Expression, Human, Location, or Number",0.65 -12,"Select the classification corresponding to the provided text among the labels: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label based on the input sentence by carrying out the classification process.",0.55 diff --git a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv b/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv deleted file mode 100644 index 0b9a03f..0000000 --- a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_42.csv +++ /dev/null @@ -1,1178 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -1,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -1,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -1,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.3 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -1,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -1,"As a categorizer, identify the category that best fits these labels by choosing one of the following options: Description, Entity",0.25 -1,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -2,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -2,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -2,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.3 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -2,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] -'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category",0.25 -2,"As a categorizer, identify the category that best fits these labels by choosing one of the following options: Description, Entity",0.25 -2,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -3,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -3,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -3,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.3 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -3,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -3,"Assign this sentence to one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.2 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] -'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category",0.25 -3,"As a categorizer, identify the category that best fits these labels by choosing one of the following options: Description, Entity",0.25 -3,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -4,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -4,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -4,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.3 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -4,"Here's the step-by-step process to generate a better prompt: - -**1. Identify the different parts between Prompt 1 and Prompt 2:** - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -**2. Randomly mutate the different parts:** - -* ""Please pick the category"" -> ""Classify based on query"" -* ""that matches this sentence"" -> ""based on the input sentence"" -* ""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -**3. Crossover the different parts with Prompt 3 and generate",0.3 -4,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.3 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] -'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category",0.25 -4,"As a categorizer, identify the category that best fits these labels by choosing one of the following options: Description, Entity",0.25 -4,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.15 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -5,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -5,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -5,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -5,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -5,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.3 -5,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Classify the question"" -* ""that matches this sentence"" -> ""based on the input"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -2. Randomly mutate the different parts (continued): -* 'Category sunt",0.4 -5,"As a categorizer, identify the category that best fits these labels by choosing one of the following options: Description, Entity",0.25 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -6,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -6,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -6,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -6,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -6,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -6,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Classify the question"" -* ""that matches this sentence"" -> ""based on the input"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -2. Randomly mutate the different parts (continued): -* 'Category sunt",0.4 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.3 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -7,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -7,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -7,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -7,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -7,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -7,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Classify the question"" -* ""that matches this sentence"" -> ""based on the input"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -2. Randomly mutate the different parts (continued): -* 'Category sunt",0.4 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.3 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -8,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -8,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -8,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -8,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -8,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -8,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Classify the question"" -* ""that matches this sentence"" -> ""based on the input"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -2. Randomly mutate the different parts (continued): -* 'Category sunt",0.4 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.3 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -9,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.45 -9,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -9,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -9,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -9,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -9,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Classify the question"" -* ""that matches this sentence"" -> ""based on the input"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -2. Randomly mutate the different parts (continued): -* 'Category sunt",0.4 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.3 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -10,"I'll follow your instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', '",0.5 -10,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -10,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -10,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -10,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.45 -10,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt",0.4 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -11,"I'll follow your instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', '",0.5 -11,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -11,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -11,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -11,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.45 -11,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt",0.4 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.5 -12,"I'll follow your instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', '",0.5 -12,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2",0.35 -12,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -12,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.4 -12,"Here's the step-by-step generation of a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Please pick the category"" vs ""Please perform Question Classification task"" -* ""that matches this sentence"" vs ""Given the question, assign a label"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Please pick the category"" -> ""Determine the category"" -* ""that matches this sentence"" -> ""based on the query"" -* ""'Description, Entity, Expression, Human, Location, or Number'"" -> ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] -* 'Planning, Ideas, FirstSteps, Idea Planner, Strategy Designer' becomes 'Category sunt",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -""Please pick the category"" vs ""Please perform Question Classification task"" -""that matches this sentence"" vs ""Given the question, assign a label"" -""Description, Entity, Expression, Human, Location, or Number"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -""Please pick the category"" -> ""Determine the category"" -""that matches this sentence"" -> ""based on the query"" -""Description, Entity, Expression, Human, Location, or Number"" -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with the following Prompt 3 and generate a final prompt",0.45 -12,"I'd be happy to help you generate a better prompt. Here are the step-by-step instructions: - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt",0.4 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instructions step-by-step to generate a better prompt. -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""Let's follow the instructions"" vs ""Please perform Question Classification task"" -* ""to generate a better prompt"" vs ""Given the question, assign a label"" -* ""[] [] [] [] [] []"" vs 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' - -2. Randomly mutate the different parts: - -* ""Let's follow the instructions"" -> ""In this task"" -* ""to generate a better prompt"" -> ""and categorize the query"" -* 'Description', 'Entity', 'Expression', 'Human', 'Location', 'Number' -> ['Category1', 'Category2', 'Category3', 'Category4', 'Category5', 'Category6'] - -3. Crossover the different parts with Prompt 3 and generate a final prompt bracket",0.45 diff --git a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 0fe1a4f..0000000 --- a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,886 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -1,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -1,"Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -1,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number. - -The different parts are: - -""Your task is to choose a type of the question"" vs ""Determine the category that best describes this sentence"" -""from Description, Entity, Expression, Human, Location and Number"" vs no equivalent part in Prompt 2 -=torch= ""from Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""The objective is to categorize a sentence"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number"" - Torch="" includes"" -> ""includes"" - -3. Crossover the different parts with the following Prompt 3",0.2 -1,"Identify the nature of the provided statement based on the options - Description, Entity, Expression, Human, Location, or Number",0.2 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -1,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -2,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -2,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -2,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -2,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number. - -The different parts are: - -""Your task is to choose a type of the question"" vs ""Determine the category that best describes this sentence"" -""from Description, Entity, Expression, Human, Location and Number"" vs no equivalent part in Prompt 2 -=torch= ""from Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""The objective is to categorize a sentence"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number"" - Torch="" includes"" -> ""includes"" - -3. Crossover the different parts with the following Prompt 3",0.2 -2,"Identify the nature of the provided statement based on the options - Description, Entity, Expression, Human, Location, or Number",0.2 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -2,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -3,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -3,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -3,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -3,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number. - -The different parts are: - -""Your task is to choose a type of the question"" vs ""Determine the category that best describes this sentence"" -""from Description, Entity, Expression, Human, Location and Number"" vs no equivalent part in Prompt 2 -=torch= ""from Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""The objective is to categorize a sentence"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number"" - Torch="" includes"" -> ""includes"" - -3. Crossover the different parts with the following Prompt 3",0.2 -3,"Identify the nature of the provided statement based on the options - Description, Entity, Expression, Human, Location, or Number",0.2 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -3,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -4,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -4,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -4,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -4,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number. - -The different parts are: - -""Your task is to choose a type of the question"" vs ""Determine the category that best describes this sentence"" -""from Description, Entity, Expression, Human, Location and Number"" vs no equivalent part in Prompt 2 -=torch= ""from Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""The objective is to categorize a sentence"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number"" - Torch="" includes"" -> ""includes"" - -3. Crossover the different parts with the following Prompt 3",0.2 -4,"Identify the nature of the provided statement based on the options - Description, Entity, Expression, Human, Location, or Number",0.2 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -4,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -5,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -5,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -5,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -5,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -5,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number. - -The different parts are: - -""Your task is to choose a type of the question"" vs ""Determine the category that best describes this sentence"" -""from Description, Entity, Expression, Human, Location and Number"" vs no equivalent part in Prompt 2 -=torch= ""from Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""The objective is to categorize a sentence"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number"" - Torch="" includes"" -> ""includes"" - -3. Crossover the different parts with the following Prompt 3",0.2 -5,"As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -5,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -6,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -6,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -6,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -6,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -6,"Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.15 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number. - -The different parts are: - -""Your task is to choose a type of the question"" vs ""Determine the category that best describes this sentence"" -""from Description, Entity, Expression, Human, Location and Number"" vs no equivalent part in Prompt 2 -=torch= ""from Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Your task is to choose a type of the question"" -> ""The objective is to categorize a sentence"" -""from Description, Entity, Expression, Human, Location and Number"" -> ""by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number"" - Torch="" includes"" -> ""includes"" - -3. Crossover the different parts with the following Prompt 3",0.2 -6,"As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -6,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -7,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -7,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -7,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -7,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.2 -7,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -3. Crossover the different parts with the",0.3 -7,"The objective is to categorize a sentence by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number. This includes classifying each sentence and subsequently create a trainable text classification model.",0.3 -7,"As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -7,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -8,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -8,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -8,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity",0.25 -8,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -3. Crossover the different parts with the",0.3 -8,"The objective is to categorize a sentence by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number. This includes classifying each sentence and subsequently create a trainable text classification model.",0.3 -8,"As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -8,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -9,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -9,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.25 -9,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity",0.25 -9,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -3. Crossover the different parts with the",0.3 -9,"The objective is to categorize a sentence by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number. This includes classifying each sentence and subsequently create a trainable text classification model.",0.3 -9,"As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -9,"Assign an annotation to the given sentence based on the following options: Description, Entity, Expression, Human, Location, or Number.",0.45 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -10,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Different parts: - -* ""Your task is to choose"" vs ""You are given a question and need to detect"" -* ""a type of the question"" vs ""which category"" -* ""Description, Entity, Expression, Human, Location and Number"" vs not changed - -2. Randomly mutate the different parts: - -* ""Your task is to choose"" -> ""The goal is to determine"" -* ""a type of the question"" -> ""the nature of the inquiry"" -* ""Description, Entity, Expression, Human, Location and Number"" -> ""categories: Description, Entity, Expression, Human, Location, and Number Type"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt: - -Prompt 3: Identify the category that corresponds to this sentence:",0.5 -10,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity",0.25 -10,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -3. Crossover the different parts with the",0.3 -10,"The objective is to categorize a sentence by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number. This includes classifying each sentence and subsequently create a trainable text classification model.",0.3 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature",0.4 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -10,"Let's follow the instructions step-by-step. - -**1. Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Different parts: - -* ""Categorize those questions"" vs ""You are given a question"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"" - -**2. Randomly mutate the different parts** - -* ""Categorize those questions"" -> ""Determine the type of the inquiry"" -* ""You are given a question"" -> ""An inquiry is presented"" -* ""Description, Entity, Expression, Human, Location, or Number"" -> ""Entity, Expression, Number"" -* ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"" -> ""an Entity"", ""a Description"", ""a Number"" - -**3. Crossover the different parts",0.5 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -11,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Different parts: - -* ""Your task is to choose"" vs ""You are given a question and need to detect"" -* ""a type of the question"" vs ""which category"" -* ""Description, Entity, Expression, Human, Location and Number"" vs not changed - -2. Randomly mutate the different parts: - -* ""Your task is to choose"" -> ""The goal is to determine"" -* ""a type of the question"" -> ""the nature of the inquiry"" -* ""Description, Entity, Expression, Human, Location and Number"" -> ""categories: Description, Entity, Expression, Human, Location, and Number Type"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt: - -Prompt 3: Identify the category that corresponds to this sentence:",0.5 -11,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -11,"I'd be happy to help you generate a better prompt! - -Based on the instructions, I identified the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -* ""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -* ""choose from"" vs ""classify it as one of the following options"" - -I randomly mutated these parts as follows: - -* ""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -* ""Description, Entity, Expression, Human, Location and Number"" -> ""categories: Description, Entity, Expression, Human, Location, or Number Type"" -* ""choose from"" -> ""identify within"" - -I then crossed the mutated parts with Prompt 3 to generate a final",0.3 -11,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -3. Crossover the different parts with the",0.3 -11,"The objective is to categorize a sentence by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number. This includes classifying each sentence and subsequently create a trainable text classification model.",0.3 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature",0.4 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""Please select the correct classification for this sentence"" -""choose from"" vs ""Description, Entity, Expression, Human, Location, or Number"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Classify the given statement"" -""choose from"" -> ""options from the list"" - -Let's mutate the parts and generate a new prompt: - -**New Prompt 1:** Classify the given statement and respond with one of the options from the list. - -Please wait for further instructions to proceed with the next steps.",0.2 -11,"Let's follow the instructions step-by-step. - -**1. Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Different parts: - -* ""Categorize those questions"" vs ""You are given a question"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"" - -**2. Randomly mutate the different parts** - -* ""Categorize those questions"" -> ""Determine the type of the inquiry"" -* ""You are given a question"" -> ""An inquiry is presented"" -* ""Description, Entity, Expression, Human, Location, or Number"" -> ""Entity, Expression, Number"" -* ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"" -> ""an Entity"", ""a Description"", ""a Number"" - -**3. Crossover the different parts",0.5 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45 -12,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. -Prompt 2: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Different parts: - -* ""Your task is to choose"" vs ""You are given a question and need to detect"" -* ""a type of the question"" vs ""which category"" -* ""Description, Entity, Expression, Human, Location and Number"" vs not changed - -2. Randomly mutate the different parts: - -* ""Your task is to choose"" -> ""The goal is to determine"" -* ""a type of the question"" -> ""the nature of the inquiry"" -* ""Description, Entity, Expression, Human, Location and Number"" -> ""categories: Description, Entity, Expression, Human, Location, and Number Type"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt: - -Prompt 3: Identify the category that corresponds to this sentence:",0.5 -12,"Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number.",0.35 -12,"Analyze the categorical nature of the information and identify the category within categories: Description, Entity, Expression, Human, Location, or Number Type, as a categorization expert, while considering the context and meaning of the text.",0.45 -12,"Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -3. Crossover the different parts with the",0.3 -12,"The objective is to categorize a sentence by selecting one of the following categories: Description, Entity, Expression, Human, Location, Number. This includes classifying each sentence and subsequently create a trainable text classification model.",0.3 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Let's follow the instruction step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -2. Randomly mutate the different parts: - -""Determine the type of the given question"" -> ""Analyze the categorical nature",0.4 -12,"Based on the instructions, I will proceed with the next steps. - -**1. Identify the different parts between the Prompt 1 and Prompt 2:** - -Prompt 1: Determine the type of the given question and choose from Description, Entity, Expression, Human, Location, and Number. - -Prompt 2: As a categorization expert, define the category of the text and classify it as one of the following options: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Determine the type of the given question"" vs ""As a categorization expert, define the category of the text"" -""Description, Entity, Expression, Human, Location and Number"" vs ""Description, Entity, Expression, Human, Location, or Number"" -""choose from"" vs ""classify it as one of the following options"" - -**2. Randomly mutate the different parts:** - -""Determine the type of the given question"" -> ""Analyze the categorical nature of the information"" -""Description, Entity, Expression, Human, Location and Number"" -> ""categories such as Description, Entity, or Expression"" -""choose from"" -> ""select from"" -""classify it as one of the following options"" -> ""assign to one of the specified categories"" - -**3. C",0.3 -12,"Let's follow the instructions step-by-step. - -**1. Identify the different parts between Prompt 1 and Prompt 2** - -Prompt 1: Categorize those questions as one of the following: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Different parts: - -* ""Categorize those questions"" vs ""You are given a question"" -* ""Description, Entity, Expression, Human, Location, or Number"" vs ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"" - -**2. Randomly mutate the different parts** - -* ""Categorize those questions"" -> ""Determine the type of the inquiry"" -* ""You are given a question"" -> ""An inquiry is presented"" -* ""Description, Entity, Expression, Human, Location, or Number"" -> ""Entity, Expression, Number"" -* ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"" -> ""an Entity"", ""a Description"", ""a Number"" - -**3. Crossover the different parts",0.5 diff --git a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv b/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv deleted file mode 100644 index 19be430..0000000 --- a/logs/experiment/trec_evopromptde_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_69.csv +++ /dev/null @@ -1,877 +0,0 @@ -step,prompt,score -1,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -1,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -1,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Please perform Question Classification task"" vs ""Using the following categories"" -""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: -""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the following Prompt 3",0.3 -1,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -1,"Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number.",0.1 -1,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.1 -1,The objective is to select a category of the statement and analyze this passage based on the specified labels: Description,0.15 -1,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -2,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -2,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -2,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -2,"I'd be happy to help you generate a better prompt! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutate and crossover the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label",0.3 -2,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.1 -2,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -2,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -3,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -3,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -3,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -3,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -3,"I'd be happy to help you generate a better prompt! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutate and crossover the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label",0.3 -3,"Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number.",0.1 -3,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -4,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -4,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -4,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -4,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -4,"I'd be happy to help you generate a better prompt! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutate and crossover the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label",0.3 -4,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the different parts with the following Prompt 3",0.25 -4,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -5,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -5,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -5,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -5,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -5,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -5,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -5,"I'd be happy to help you generate a better prompt! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutate and crossover the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label",0.3 -5,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the different parts with the following Prompt 3",0.25 -5,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -5,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -6,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -6,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -6,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -6,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -6,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -6,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -6,"I'd be happy to help you generate a better prompt! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutate and crossover the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label",0.3 -6,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the different parts with the following Prompt 3",0.25 -6,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -6,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -7,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -7,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -7,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -7,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -7,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -7,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -7,"I'd be happy to help you generate a better prompt! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutate and crossover the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label",0.3 -7,"Here are the steps to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the different parts with the following Prompt 3",0.25 -7,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -7,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -8,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -8,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -8,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -8,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -8,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -8,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -8,"You'd like me to follow the instructions step-by-step to generate a better prompt. Let's go! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutating and crossing over the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', '",0.4 -8,"I'd be happy to help you generate a better prompt. - -Here are the steps followed: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the",0.45 -8,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -8,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -9,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -9,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -9,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -9,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -9,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -9,"Classify this sentence by choosing one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.15 -9,"You'd like me to follow the instructions step-by-step to generate a better prompt. Let's go! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutating and crossing over the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', '",0.4 -9,"I'd be happy to help you generate a better prompt. - -Here are the steps followed: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the",0.45 -9,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -9,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -10,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -10,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -10,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -10,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -10,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -10,"The objective is to identify the category for the content of this passage, based on one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -10,"You'd like me to follow the instructions step-by-step to generate a better prompt. Let's go! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutating and crossing over the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', '",0.4 -10,"I'd be happy to help you generate a better prompt. - -Here are the steps followed: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the",0.45 -10,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -10,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -11,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -11,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -11,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -11,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -11,"The objective is to identify the category for the content of this passage, based on one of the following options: Description, Entity, Expression, Human, Location, or Number.",0.25 -11,"You'd like me to follow the instructions step-by-step to generate a better prompt. Let's go! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutating and crossing over the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', '",0.4 -11,"I'd be happy to help you generate a better prompt. - -Here are the steps followed: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the",0.45 -11,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 -12,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5 -12,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.4 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -12,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.3 -12,"Here's the step-by-step process to generate a better prompt: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the given passage"" - -3. Crossover the different parts with the",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: ""Your task is to choose a type of the question"", ""from Description, Entity, Expression, Human, Location and Number"", and ""Question Classification task"". -Prompt 2: ""Please perform"", ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"". - -Different parts: - -* ""Your task is to choose a type of the question"" vs ""Please perform"" -* ""from Description, Entity, Expression, Human, Location and Number"" vs ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']"" -* ""Question Classification task"" vs no change - -2. Randomly mutate the different parts: - -* ""Your task is to choose a type of the question"" -> ""The goal is to categorize the inquiry"" -* ""from Description, Entity, Expression, Human, Location and Number"" -> ""based on the options: Description, Entity, Expression, Human, Location, or Number"" -* ""Question Classification task",0.3 -12,"You'd like me to follow the instructions step-by-step to generate a better prompt. Let's go! - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: I'll follow the instructions step-by-step to generate a better prompt. - -Prompt 2: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text - -Different parts: - -* ""I'll follow the instructions step-by-step to generate a better prompt"" vs. (no match) -* ""Please perform Question Classification task"" vs. ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs. ""classify this sentence"" - -Since Prompt 1 does not have matching parts with Prompt 2, we can proceed with mutating and crossing over the different parts. - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Classify this text using the categories provided"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', '",0.4 -12,"I'd be happy to help you generate a better prompt. - -Here are the steps followed: - -1. Identify the different parts between Prompt 1 and Prompt 2: - -Prompt 1: Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -* ""Please perform Question Classification task"" vs ""Using the following categories"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" vs ""classify this sentence"" - -2. Randomly mutate the different parts: - -* ""Please perform Question Classification task"" -> ""Analyze this text using the categorization framework"" -* ""Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text"" -> ""based on the sentence content, identify its corresponding label"" -* ""this sentence"" -> ""the passage"" - -3. Crossover the",0.45 -12,"Select a category and assess the statement in this passage based on the specified labels: Description, Entity, Expression, Human, Location, or Number.",0.35 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Identify the different parts between the Prompt 1 and Prompt 2: -Prompt 1: Classify this sentence based on the provided categories: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify this sentence into one of these categories: Description, Entity, Expression, Human, Location, or Number. - -Different parts: - -""Classify this sentence based on the provided categories"" vs ""Classify this sentence into one of these categories"" -""This sentence"" vs ""this sentence"" -""provided categories"" vs ""these categories"" - -Let me know when to proceed to the next step!",0.1 diff --git a/logs/experiment/trec_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv b/logs/experiment/trec_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv deleted file mode 100644 index ef43411..0000000 --- a/logs/experiment/trec_evopromptga_meta-llama/Meta-Llama-3-70B-Instruct_meta-llama/Meta-Llama-3-70B-Instruct_42.csv +++ /dev/null @@ -1,133 +0,0 @@ -step,prompt,score -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.9 -1,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.85 -1,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.85 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.55 -1,"I'll follow the instructions step-by-step to generate a better prompt. - -1. Crossover Prompt: Classify the sentence into one of the following categories: Description, Entity, Expression, Human, Location, or Number. - -2. , , , , , or ) that best matches the sentence, along with the corresponding question type.",0.4 -10,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -10,"Classify the given question's categorical type from a predetermined set of labels, including 'Description', 'Entity', 'Expression', 'Human', 'Location', and 'Number'",0.4 -11,"Assign the given sentence to one of the six categories (Description, Entity, Expression, Human, Location, or Number) and indicate the corresponding question type.",0.5 -11,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.45 -11,"Classify the type of question by selecting one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -11,"Classify the question type of each statement into Description, Entity, Expression, Human, Location, or Number.",0.45 -11,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.4 -11,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -11,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Crossover the prompts** - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify the given question according to its category, selecting one of the predefined labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the assigned label. - -Crossover Prompt: Classify the given sentence into one of the predefined categories: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. - -**Step 2: Mutate the prompt** - -Mutated Prompt: [Find the appropriate label from the category list: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] and categorize the given sentence.]",0.4 -11,"Identify the category (, , , , , or ) that best matches the sentence, along with the corresponding question type.",0.4 -11,"Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.",0.4 -11,"Classify the given question's categorical type from a predetermined set of labels, including 'Description', 'Entity', 'Expression', 'Human', 'Location', and 'Number'",0.4 -12,"Assign the given sentence to one of the six categories (Description, Entity, Expression, Human, Location, or Number) and indicate the corresponding question type.",0.5 -12,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.45 -12,"Indicate the type of question, selecting from: Description, Entity, Expression, Human, Location, or Number",0.45 -12,"Classify the type of question by selecting one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.45 -12,"Classify the question type of each statement into Description, Entity, Expression, Human, Location, or Number.",0.45 -12,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.4 -12,"Transform the given instructions into a category label, selecting one of the predefined options: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Follow the sequence of actions to reach the correct categorization.",0.4 -12,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.4 -12,"Let's follow the instructions step-by-step to generate a better prompt. - -**Step 1: Crossover the prompts** - -Prompt 1: Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Classify the given question according to its category, selecting one of the predefined labels: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return the assigned label. - -Crossover Prompt: Classify the given sentence into one of the predefined categories: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. - -**Step 2: Mutate the prompt** - -Mutated Prompt: [Find the appropriate label from the category list: ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] and categorize the given sentence.]",0.4 -12,"Identify the category (, , , , , or ) that best matches the sentence, along with the corresponding question type.",0.4 diff --git a/logs/experiment/trec_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv b/logs/experiment/trec_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv deleted file mode 100644 index 204d033..0000000 --- a/logs/experiment/trec_evopromptga_meta-llama/Meta-Llama-3-8B-Instruct_meta-llama/Meta-Llama-3-8B-Instruct_47.csv +++ /dev/null @@ -1,349 +0,0 @@ -step,prompt,score -1,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.5 -1,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -1,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -1,"Identify the categorization or type of the sentence or question provided, selecting from the options: Description, Entity, Expression, Human, Location, or Number.",0.35 -1,"Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number.",0.3 -1,"Let's follow the instructions step-by-step to generate a better prompt! - -**Step 1: Crossover** - -Prompt 1: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Combined Prompt: -""Classify the given question into the most relevant category, choosing from 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'."" - -**Step 2: Mutation** - -| Remove the phrase ""given question"" and make it more concise to focus on the classification task.",0.25 -1,"Distinguish the most suitable category for the presented question, selecting from the options: Description, Entity, Expression, Human, Location, or Number.",0.25 -1,"Classify the given sentence into one of the following categories: Description, Entity, Expression, Human, Location, or Number.",0.25 -1,"Determine the category that best describes this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -1,"Choose the appropriate classification for this sentence: Description, Entity, Expression, Human, Location, or Number.",0.2 -2,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.5 -2,"Determine the best-fitting category for the presented question, narrowing down the list of possibilities to Description, Entity, Expression, Human, Location, or Number.",0.4 -2,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -2,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -2,"Identify the categorization or type of the sentence or question provided, selecting from the options: Description, Entity, Expression, Human, Location, or Number.",0.35 -2,"I'd be happy to help! - -1. Crossover the prompts: - -Prompt 1: Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -Crossover Prompt: Identify the category that corresponds to the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -2. Mutate the prompt generated in Step 1: - -[prompt]Categorize the given question into one of the following categories: Description, Entity, Expression, Human, Location, or Number.[/prompt] - -The final mutated prompt is: - -[prompt]Categorize the given question into one of the following categories: Description, Entity, Expression, Human, Location, or Number.[/prompt]",0.35 -2,"Choose the most fitting category for the classification: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'",0.35 -2,"Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number.",0.3 -2,"Let's follow the instructions step-by-step to generate a better prompt! - -**Step 1: Crossover** - -Prompt 1: You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"". - -Prompt 2: Using the following categories, classify this sentence: Description, Entity, Expression, Human, Location, or Number. - -Combined Prompt: -""Classify the given question into the most relevant category, choosing from 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'."" - -**Step 2: Mutation** - -| Remove the phrase ""given question"" and make it more concise to focus on the classification task.",0.25 -2,"Distinguish the most suitable category for the presented question, selecting from the options: Description, Entity, Expression, Human, Location, or Number.",0.25 -3,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.5 -3,"Determine the best-fitting category for the presented question, narrowing down the list of possibilities to Description, Entity, Expression, Human, Location, or Number.",0.4 -3,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -3,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -3,"Identify the categorization or type of the sentence or question provided, selecting from the options: Description, Entity, Expression, Human, Location, or Number.",0.35 -3,"I'd be happy to help! - -1. Crossover the prompts: - -Prompt 1: Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -Crossover Prompt: Identify the category that corresponds to the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -2. Mutate the prompt generated in Step 1: - -[prompt]Categorize the given question into one of the following categories: Description, Entity, Expression, Human, Location, or Number.[/prompt] - -The final mutated prompt is: - -[prompt]Categorize the given question into one of the following categories: Description, Entity, Expression, Human, Location, or Number.[/prompt]",0.35 -3,"Choose the most fitting category for the classification: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'",0.35 -3,"Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number.",0.3 -3,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Crossover the following prompts: - -Prompt 1: Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number. - -Prompt 2: Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -Generated crossover prompt: Determine the category that corresponds to the given sentence or question. - -2. Mutate the generated prompt to make it more specific and clearer: - -| Remove the phrase ""given question"" and make it more concise to focus on the classification task.",0.25 -4,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.5 -4,"Determine the best-fitting category for the presented question, narrowing down the list of possibilities to Description, Entity, Expression, Human, Location, or Number.",0.4 -4,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.35 -4,"Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -4,"Identify the categorization or type of the sentence or question provided, selecting from the options: Description, Entity, Expression, Human, Location, or Number.",0.35 -4,"I'd be happy to help! - -1. Crossover the prompts: - -Prompt 1: Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number. -Prompt 2: Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -Crossover Prompt: Identify the category that corresponds to the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -2. Mutate the prompt generated in Step 1: - -[prompt]Categorize the given question into one of the following categories: Description, Entity, Expression, Human, Location, or Number.[/prompt] - -The final mutated prompt is: - -[prompt]Categorize the given question into one of the following categories: Description, Entity, Expression, Human, Location, or Number.[/prompt]",0.35 -4,"Choose the most fitting category for the classification: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'",0.35 -4,"Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number.",0.3 -4,"Assign the most suitable classification – Description, Entity, Expression, Human, Location, or Number – to the provided context.",0.3 -4,"Let's follow the instructions step-by-step to generate a better prompt. - -1. Crossover the following prompts: - -Prompt 1: Identify the category that corresponds to this sentence: Description, Entity, Expression, Human, Location, or Number. - -Prompt 2: Determine the most fitting category for the provided question, choosing from the options: Description, Entity, Expression, Human, Location, or Number. - -Generated crossover prompt: Determine the category that corresponds to the given sentence or question. - -2. Mutate the generated prompt to make it more specific and clearer: - -Classify the question into a specific category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and produce the corresponding label only.",0.35 -7,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6 -7,"Classify a given question into one of the following categories based on its topic: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'. Provide the matching category for the question, allowing accurate identification of its categorization.",0.55 -7,"Classify the input text into one of the predefined categories ('Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number') based on its inherent subject matter.",0.5 -7,"Identify the question's topic, labeling it as 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', while considering its category.",0.45 -7,"Identify the category for the given sentence: Description, Entity, Expression, Human, Location, or Number",0.45 -7,"Classify the input text into one of the following categories: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label.",0.4 -7,"Classify the input text into a category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and respond with the matching category.",0.4 -7,"Classify the input question into the most suitable category (Description, Entity, Expression, Human, Location, or Number) and return the assigned label.",0.4 -7,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -7,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -8,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6 -8,"Classify a given question into one of the following categories based on its topic: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'. Provide the matching category for the question, allowing accurate identification of its categorization.",0.55 -8,"Classify the input text into one of the predefined categories ('Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number') based on its inherent subject matter.",0.5 -8,"Identify the question's topic, labeling it as 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', while considering its category.",0.45 -8,"Identify the category for the given sentence: Description, Entity, Expression, Human, Location, or Number",0.45 -8,"Classify the input text into one of the following categories: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label.",0.4 -8,"Classify the input text into a category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and respond with the matching category.",0.4 -8,"Classify the input question into the most suitable category (Description, Entity, Expression, Human, Location, or Number) and return the assigned label.",0.4 -8,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -8,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -9,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6 -9,"Classify a given question into one of the following categories based on its topic: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'. Provide the matching category for the question, allowing accurate identification of its categorization.",0.55 -9,"Classify the input text into one of the predefined categories ('Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number') based on its inherent subject matter.",0.5 -9,"Identify the question's topic, labeling it as 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', while considering its category.",0.45 -9,"Identify the category for the given sentence: Description, Entity, Expression, Human, Location, or Number",0.45 -9,"Classify the input text into one of the following categories: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label.",0.4 -9,"Classify the input text into a category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and respond with the matching category.",0.4 -9,"Classify the input question into the most suitable category (Description, Entity, Expression, Human, Location, or Number) and return the assigned label.",0.4 -9,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -9,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -10,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6 -10,"Classify a given question into one of the following categories based on its topic: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'. Provide the matching category for the question, allowing accurate identification of its categorization.",0.55 -10,"Classify the input text into one of the predefined categories ('Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number') based on its inherent subject matter.",0.5 -10,"Identify the question's topic, labeling it as 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', while considering its category.",0.45 -10,"Identify the category for the given sentence: Description, Entity, Expression, Human, Location, or Number",0.45 -10,"Classify the input text into one of the following categories: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label.",0.4 -10,"Classify the input text into a category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and respond with the matching category.",0.4 -10,"Classify the input question into the most suitable category (Description, Entity, Expression, Human, Location, or Number) and return the assigned label.",0.4 -10,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -10,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -11,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6 -11,"Classify a given question into one of the following categories based on its topic: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'. Provide the matching category for the question, allowing accurate identification of its categorization.",0.55 -11,"Classify the input text into one of the predefined categories ('Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number') based on its inherent subject matter.",0.5 -11,"Identify the question's topic, labeling it as 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', while considering its category.",0.45 -11,"Identify the category for the given sentence: Description, Entity, Expression, Human, Location, or Number",0.45 -11,"Classify the input text into one of the following categories: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label.",0.4 -11,"Classify the input text into a category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and respond with the matching category.",0.4 -11,"Classify the input question into the most suitable category (Description, Entity, Expression, Human, Location, or Number) and return the assigned label.",0.4 -11,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -11,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 -12,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6 -12,"Classify a given question into one of the following categories based on its topic: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number'. Provide the matching category for the question, allowing accurate identification of its categorization.",0.55 -12,"Classify the input text into one of the predefined categories ('Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number') based on its inherent subject matter.",0.5 -12,"Identify the question's topic, labeling it as 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', while considering its category.",0.45 -12,"Identify the category for the given sentence: Description, Entity, Expression, Human, Location, or Number",0.45 -12,"Classify the input text into one of the following categories: Description, Entity, Expression, Human, Location, or Number, and provide the corresponding label.",0.4 -12,"Classify the input text into a category: 'Description', 'Entity', 'Expression', 'Human', 'Location', or 'Number', and respond with the matching category.",0.4 -12,"Classify the input question into the most suitable category (Description, Entity, Expression, Human, Location, or Number) and return the assigned label.",0.4 -12,"Please pick the category that matches this sentence: Description, Entity, Expression, Human, Location, or Number.",0.35 -12,"Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text",0.35 diff --git a/logs/experiment_eval/best_scores.csv b/logs/experiment_eval/best_scores.csv deleted file mode 100644 index df294d9..0000000 --- a/logs/experiment_eval/best_scores.csv +++ /dev/null @@ -1,106 +0,0 @@ -task,optimizer,meta_llm,downstream_llm,evaluation_llm,random_seed,prompt,train_score,test_score -agnews,evopromptde,meta-llama\Meta-Llama-3-70B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-70B-Instruct,42,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",1.0,0.89 -agnews,evopromptde,meta-llama\Meta-Llama-3-70B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-70B-Instruct,47,"Your task is to identify the subject of the news piece and classify it into one of four categories: World, Sports, Business and Tech.",0.95,0.875 -agnews,evopromptde,meta-llama\Meta-Llama-3-70B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-70B-Instruct,69,"The goal is to identify the journal article according to its primary theme and determine whether it belongs to the World, Sports, Business, or Tech category. ""The goal is to determine"" -* ""a type of the question"" -> ""the nature of the inquiry"" -* ""Description, Entity, Expression, Human, Location and Number"" -> ""categories: Description, Entity, Expression, Human, Location, and Number Type"" - -3. Crossover the different parts with Prompt 3 and generate a final prompt: - -Prompt 3: Identify the category that corresponds to this sentence:",0.5,0.27 -trec,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.",0.5,0.44 -trec,evopromptga,meta-llama\Meta-Llama-3-70B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-70B-Instruct,42,"Identify the most suitable category (Description, Entity, Expression, Human, Location, or Number) for the provided text or question.",0.95,0.63 -trec,evopromptga,meta-llama\Meta-Llama-3-70B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-70B-Instruct,47,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.75,0.73 -trec,evopromptga,meta-llama\Meta-Llama-3-70B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-70B-Instruct,69,"Categorize the question into one of the six types - Description, Entity, Expression, Human, Location, or Number - and provide the relevant label.",0.9,0.62 -trec,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Assign the given sentence to one of the six categories (Description, Entity, Expression, Human, Location, or Number) and indicate the corresponding question type.",0.5,0.515 -trec,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.5,0.685 -trec,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.",0.6,0.66 diff --git a/logs/experiment_eval_task_descr/best_scores.csv b/logs/experiment_eval_task_descr/best_scores.csv deleted file mode 100644 index 2a168c9..0000000 --- a/logs/experiment_eval_task_descr/best_scores.csv +++ /dev/null @@ -1,43 +0,0 @@ -task,optimizer,meta_llm,downstream_llm,evaluation_llm,random_seed,prompt,train_score,test_score -agnews,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.",0.95,0.885 -agnews,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"Your task is to classify the news item as ""World"", ""Sports"", ""Tech"" or ""Business"".",0.9,0.89 -agnews,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"The objective is to assign a news article to one of the following categories: World, Sports, Business, or Tech, based on its main topic.",1.0,0.88 -agnews,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Choose a word from World, Sports, Business and Tech to categorize the given text.",1.0,0.83 -agnews,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"Classify the given news article into one of the four categories ['World', 'Sports', 'Business', or 'Tech'] based on its primary theme and main topic, ensuring accurate categorization.",1.0,0.88 -agnews,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Classify the given news article into one of the four main categories: World, Sports, Business, or Tech, based on the article's topic.",0.95,0.87 -cr,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Consider customer reviews and analyze them to determine their emotional tone, classifying them as expressing either positive or negative sentiment.",0.95,0.785 -cr,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"As a sentiment classifier, examine the text passage for sentiment in customer reviews, by assessing the overall emotional direction, and classify the expression as either positive or negative.",0.95,0.855 -cr,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"You will be tasked with analyzing text to determine its emotional tone, identifying whether it expresses a positive or negative sentiment, while considering the broader context.",1.0,0.855 -cr,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Classify this customer review as expressing either a ""positive"" or ""negative"" sentiment, analyzing its tone and content.",1.0,0.94 -cr,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"Classify the provided text as having positive or negative sentiment, determining the corresponding sentiment label from ['negative', 'positive'].",1.0,0.93 -cr,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Given a review, classify it as expressing a positive or negative sentiment.",0.95,0.93 -mr,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Given a tweet, classify it as having a positive or negative sentiment.",0.95,0.885 -mr,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"To categorize the sentiment of text snippets in a movie review, classify the sentiment as either 'negative' or 'positive', taking into consideration",0.95,0.76 -mr,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Given a tweet, classify it as having a positive or negative sentiment.",0.95,0.89 -mr,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,Classify a movie review as expressing either 'negative' or 'positive' sentiment by identifying the underlying emotion.,1.0,0.915 -mr,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"Based on the provided movie review, please classify its sentiment as either ""positive"" or ""negative"" according to the given binary sentiment annotations.",1.0,0.905 -mr,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Classify the movie review text, determining whether it expresses a positive or negative sentiment, and output the corresponding label ('positive' or 'negative')",1.0,0.85 -sst-5,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.",0.5,0.45 -sst-5,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"The object is to classify movie reviews into one of the following categories: terrible, bad, okay, good, or great, based on the sentiment.",0.65,0.57 -sst-5,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Analyze movie reviews and classify them into one of the following categories: terrible, bad, okay, good, or great.",0.55,0.4 -sst-5,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Classify the sentiment of the given movie review among ['terrible', 'bad', 'okay', 'good', 'great'] based on its emotional tone, leveraging your language understanding",0.65,0.565 -sst-5,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good",0.65,0.49 -sst-5,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.",0.6,0.47 -sst2,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0,0.94 -sst2,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"As a sentiment classifier, consider a movie review sentence and analyze its emotional tone, classifying it as either positive or negative sentiment, while taking into account its meaning and context.",1.0,0.815 -sst2,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Examine the written opinion in a movie review and classify it as either ""positive"" or ""negative"", considering the sentence meaning and relevant context.",1.0,0.88 -sst2,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.",1.0,0.945 -sst2,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,Determine whether the given movie review expresses strongly positive or strongly negative sentiment.,1.0,0.935 -sst2,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.",1.0,0.91 -subj,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Your task is to examine sentences from movie reviews and understand the purpose of the utterance, and then determine the intention behind the statement, by classifying them as either subjective or objective",0.8,0.75 -subj,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.",0.75,0.585 -subj,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context",0.85,0.63 -subj,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues.",0.8,0.705 -subj,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics.",0.75,0.695 -subj,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Given a statement, classify it as expressing a subjective or objective opinion.",0.8,0.755 -trec,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.",0.55,0.63 -trec,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"You are given a question. You need to detect which category better describes the question. Answer with ""Description"", ""Entity"", ""Expression"", ""Human"", ""Location"", and ""Number"".",0.55,0.645 -trec,evopromptde,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.",0.45,0.75 -trec,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,42,"Please classify each question into its primary category from the following options: Description, Entity, Expression, Human, Location, Number.",0.65,0.605 -trec,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,47,"For each question, select the primary category (Description, Entity, Expression, Human, Location, or Number).",0.55,0.685 -trec,evopromptga,meta-llama\Meta-Llama-3-8B-Instruct,meta-llama/Meta-Llama-3-70B-Instruct,meta-llama\Meta-Llama-3-8B-Instruct,69,"Recognize the primary category of each question, choosing from Description, Entity, Expression, Human, Location, or Number, and return the most fitting label.",0.7,0.615 diff --git a/logs/experiment_gpt/best_prompts.csv b/logs/experiment_gpt/best_prompts.csv deleted file mode 100644 index fdc81de..0000000 --- a/logs/experiment_gpt/best_prompts.csv +++ /dev/null @@ -1,8 +0,0 @@ -,prompt,task,score -0,"Choose a word from World, Sports, Business and Tech to categorize the given text.",agnews,1.0 -1,"Classify the provided text as either ""positive"" or ""negative"" based on its emotional sentiment.",cr,1.0 -2,"Ascertain the underlying emotional tone of the input text, categorizing it as either unambiguously positive, unambiguously negative, neutral, or a nuanced blend of both sentiment options.",mr,1.0 -3,"Determine the sentiment of the given text by labeling it as 'terrible', 'bad', 'okay', 'good', or 'great' according to the author's expressed emotions.",sst-5,0.8 -4,"Label the provided sentence or comment as ""positive"" or ""negative"" based on its tone and sentiment.",sst2,1.0 -5,"Given an expression, you need to judge whether it represents subjective or objective opinion by assessing the context and meaning.",subj,0.9 -6,"Identify the most suitable category (Description, Entity, Expression, Human, Location, or Number) for the provided text or question.",trec,0.95 diff --git a/logs/experiment_gpt/best_scores.csv b/logs/experiment_gpt/best_scores.csv deleted file mode 100644 index d7540c8..0000000 --- a/logs/experiment_gpt/best_scores.csv +++ /dev/null @@ -1,13 +0,0 @@ -task,optimizer,meta_llm,downstream_llm,evaluation_llm,random_seed,use_task_desc,train_score,test_score -agnews,evopromptde,meta-llama\Meta-Llama-3-70B-Instruct,gpt-4o-2024-05-13,meta-llama\Meta-Llama-3-70B-Instruct,42,"Classify the topic of the following news as ""World"", ""Sports"", ""Tech"" or ""Business"".",1.0,0.825 -agnews,evopromptde,meta-llama\Meta-Llama-3-70B-Instruct,gpt-4o-2024-05-13,meta-llama\Meta-Llama-3-70B-Instruct,47,"Your task is to identify the subject of the news piece and classify it into one of four categories: World, Sports, Business and Tech.",0.95,0.825 -agnews,evopromptde,meta-llama\Meta-Llama-3-70B-Instruct,gpt-4o-2024-05-13,meta-llama\Meta-Llama-3-70B-Instruct,69,"The goal is to identify the journal article according to its primary theme and determine whether it belongs to the World, Sports, Business, or Tech category." - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_plot = best_scores_all[best_scores_all[\"optimizer\"] == \"GA\"].copy()\n", - "init_prompts = best_scores_all[best_scores_all[\"optimizer\"] == \"init\"]\n", - "\n", - "df_plot = pd.concat([df_plot, init_prompts])\n", - "plot_bar(df_plot, \"meta_llm\", title=\"EvoPrompt GA\")" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "df_plot = best_scores_all[best_scores_all[\"optimizer\"] == \"DE\"].copy()\n", - "init_prompts = best_scores_all[(best_scores_all[\"optimizer\"] == \"init\")]\n", - "\n", - "df_plot = pd.concat([df_plot, init_prompts])" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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taskAverageagnewscrmrsst-5sst2subjtrec
meta_llmdownstream_llmoptimizeruse_task_desc
Llama-3-70BLlama-3-70BDEFalse79.7486.67 ± 0.2991.83 ± 0.7691.50 ± 0.8750.17 ± 3.2595.17 ± 3.6970.67 ± 6.8372.17 ± 3.69
GAFalse78.2685.50 ± 2.2989.83 ± 2.2588.83 ± 4.6551.67 ± 2.2595.33 ± 2.0268.83 ± 8.7567.83 ± 5.77
Llama-3-8BLlama-3-70BDEFalse74.5285.00 ± 1.8091.67 ± 1.1590.67 ± 2.3638.00 ± 14.3192.00 ± 8.6758.00 ± 10.4466.33 ± 16.74
GAFalse74.5086.50 ± 1.5083.00 ± 5.0784.33 ± 5.3051.67 ± 2.3691.33 ± 3.7961.00 ± 7.0063.67 ± 10.05
initLlama-3-70BinitFalse72.8186.67 ± 2.0293.17 ± 1.7689.67 ± 4.4839.83 ± 31.3293.17 ± 3.6954.00 ± 7.2653.17 ± 17.86
\n", - "
" - ], - "text/plain": [ - "task Average agnews \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 79.74 86.67 ± 0.29 \n", - " GA False 78.26 85.50 ± 2.29 \n", - "Llama-3-8B Llama-3-70B DE False 74.52 85.00 ± 1.80 \n", - " GA False 74.50 86.50 ± 1.50 \n", - "init Llama-3-70B init False 72.81 86.67 ± 2.02 \n", - "\n", - "task cr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 91.83 ± 0.76 \n", - " GA False 89.83 ± 2.25 \n", - "Llama-3-8B Llama-3-70B DE False 91.67 ± 1.15 \n", - " GA False 83.00 ± 5.07 \n", - "init Llama-3-70B init False 93.17 ± 1.76 \n", - "\n", - "task mr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 91.50 ± 0.87 \n", - " GA False 88.83 ± 4.65 \n", - "Llama-3-8B Llama-3-70B DE False 90.67 ± 2.36 \n", - " GA False 84.33 ± 5.30 \n", - "init Llama-3-70B init False 89.67 ± 4.48 \n", - "\n", - "task sst-5 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 50.17 ± 3.25 \n", - " GA False 51.67 ± 2.25 \n", - "Llama-3-8B Llama-3-70B DE False 38.00 ± 14.31 \n", - " GA False 51.67 ± 2.36 \n", - "init Llama-3-70B init False 39.83 ± 31.32 \n", - "\n", - "task sst2 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 95.17 ± 3.69 \n", - " GA False 95.33 ± 2.02 \n", - "Llama-3-8B Llama-3-70B DE False 92.00 ± 8.67 \n", - " GA False 91.33 ± 3.79 \n", - "init Llama-3-70B init False 93.17 ± 3.69 \n", - "\n", - "task subj \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 70.67 ± 6.83 \n", - " GA False 68.83 ± 8.75 \n", - "Llama-3-8B Llama-3-70B DE False 58.00 ± 10.44 \n", - " GA False 61.00 ± 7.00 \n", - "init Llama-3-70B init False 54.00 ± 7.26 \n", - "\n", - "task trec \n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 72.17 ± 3.69 \n", - " GA False 67.83 ± 5.77 \n", - "Llama-3-8B Llama-3-70B DE False 66.33 ± 16.74 \n", - " GA False 63.67 ± 10.05 \n", - "init Llama-3-70B init False 53.17 ± 17.86 " - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_pants = format_score(best_scores_all)\n", - "get_result_table(fancy_pants)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Experiment (3): Task Descriptions" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "df_comparison = read_best_scores(\"experiment\")\n", - "df_comparison = clean_names(df_comparison)\n", - "df_comparison = df_comparison[df_comparison.meta_llm == \"Llama-3-8B\"]\n", - "df_comparison = get_mean_std(df_comparison)\n", - "df_comparison = get_avg_across_tasks(df_comparison)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "best_scores_td = read_best_scores(\"experiment-task-descr\")\n", - "best_scores_td = get_mean_std(best_scores_td)\n", - "best_scores_td = get_avg_across_tasks(best_scores_td)\n", - "best_scores_td = clean_names(best_scores_td)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskAverageagnewscrmrsst-5sst2subjtrec
meta_llmdownstream_llmoptimizeruse_task_desc
Llama-3-8BLlama-3-70BDEFalse74.5285.00 ± 1.8091.67 ± 1.1590.67 ± 2.3638.00 ± 14.3192.00 ± 8.6758.00 ± 10.4466.33 ± 16.74
True78.1287.33 ± 0.7693.00 ± 0.0087.33 ± 1.0451.33 ± 3.4094.17 ± 4.6559.17 ± 9.2574.50 ± 5.41
GAFalse74.5086.50 ± 1.5083.00 ± 5.0784.33 ± 5.3051.67 ± 2.3691.33 ± 3.7961.00 ± 7.0063.67 ± 10.05
True76.5085.67 ± 1.0489.83 ± 3.1891.17 ± 3.0146.67 ± 2.2591.50 ± 8.2666.33 ± 6.2564.33 ± 2.25
\n", - "
" - ], - "text/plain": [ - "task Average agnews \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 74.52 85.00 ± 1.80 \n", - " True 78.12 87.33 ± 0.76 \n", - " GA False 74.50 86.50 ± 1.50 \n", - " True 76.50 85.67 ± 1.04 \n", - "\n", - "task cr mr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 91.67 ± 1.15 90.67 ± 2.36 \n", - " True 93.00 ± 0.00 87.33 ± 1.04 \n", - " GA False 83.00 ± 5.07 84.33 ± 5.30 \n", - " True 89.83 ± 3.18 91.17 ± 3.01 \n", - "\n", - "task sst-5 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 38.00 ± 14.31 \n", - " True 51.33 ± 3.40 \n", - " GA False 51.67 ± 2.36 \n", - " True 46.67 ± 2.25 \n", - "\n", - "task sst2 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 92.00 ± 8.67 \n", - " True 94.17 ± 4.65 \n", - " GA False 91.33 ± 3.79 \n", - " True 91.50 ± 8.26 \n", - "\n", - "task subj \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 58.00 ± 10.44 \n", - " True 59.17 ± 9.25 \n", - " GA False 61.00 ± 7.00 \n", - " True 66.33 ± 6.25 \n", - "\n", - "task trec \n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 66.33 ± 16.74 \n", - " True 74.50 ± 5.41 \n", - " GA False 63.67 ± 10.05 \n", - " True 64.33 ± 2.25 " - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_plot = pd.concat([best_scores_td, df_comparison])\n", - "fancy_pants = format_score(df_plot)\n", - "get_result_table(fancy_pants)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_plot = pd.concat([best_scores_td, df_comparison])\n", - "df_plot = df_plot[df_plot.optimizer == \"DE\"]\n", - "plot_bar(df_plot, \"use_task_desc\", \"EvoPrompt DE\")" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_plot = pd.concat([best_scores_td, df_comparison])\n", - "df_plot = df_plot[df_plot.optimizer == \"GA\"]\n", - "plot_bar(df_plot, \"use_task_desc\", \"EvoPrompt GA\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Further evaluation: Downstream model comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "df_experiment = read_best_scores(\"experiment_gpt\")\n", - "df_experiment = df_experiment.drop(\"train_score\", axis=\"columns\")\n", - "df_experiment[\"use_task_desc\"] = False\n", - "df_experiment = clean_names(df_experiment)\n", - "df_experiment = get_mean_std(df_experiment)\n", - "df_experiment = get_avg_across_tasks(df_experiment)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "df_comparison = read_best_scores(\"experiment\")\n", - "df_comparison = clean_names(df_comparison)\n", - "df_comparison = df_comparison[df_comparison[\"meta_llm\"] == \"Llama-3-70B\"]\n", - "df_comparison = df_comparison[df_comparison[\"use_task_desc\"] == False]\n", - "df_comparison = df_comparison[df_comparison[\"optimizer\"] == \"DE\"]\n", - "df_comparison = df_comparison[df_comparison[\"task\"].isin(df_experiment.task.unique())]\n", - "\n", - "df_comparison = get_mean_std(df_comparison)\n", - "df_comparison = get_avg_across_tasks(df_comparison)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_plot = pd.concat([df_experiment, df_comparison])\n", - "plot_bar(df_plot, hue=\"downstream_llm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskAverageagnewscrsst-5subj
meta_llmdownstream_llmoptimizeruse_task_desc
Llama-3-70BLlama-3-70BDEFalse74.8386.67 ± 0.2991.83 ± 0.7650.17 ± 3.2570.67 ± 6.83
gpt-4oDEFalse73.9282.17 ± 0.5892.67 ± 3.3351.33 ± 1.5369.50 ± 2.60
\n", - "
" - ], - "text/plain": [ - "task Average agnews \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 74.83 86.67 ± 0.29 \n", - " gpt-4o DE False 73.92 82.17 ± 0.58 \n", - "\n", - "task cr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 91.83 ± 0.76 \n", - " gpt-4o DE False 92.67 ± 3.33 \n", - "\n", - "task sst-5 subj \n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 50.17 ± 3.25 70.67 ± 6.83 \n", - " gpt-4o DE False 51.33 ± 1.53 69.50 ± 2.60 " - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_pants = format_score(df_plot)\n", - "get_result_table(fancy_pants)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Further evaluation: DE vs GA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Llama-3-70B" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_comparison = read_best_scores(\"experiment\")\n", - "df_comparison = clean_names(df_comparison)\n", - "df_comparison = df_comparison[df_comparison[\"meta_llm\"] == \"Llama-3-70B\"]\n", - "\n", - "df_comparison = get_mean_std(df_comparison)\n", - "df_comparison = get_avg_across_tasks(df_comparison)\n", - "\n", - "plot_bar(df_comparison, hue=\"optimizer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskAverageagnewscrmrsst-5sst2subjtrec
meta_llmdownstream_llmoptimizeruse_task_desc
Llama-3-70BLlama-3-70BDEFalse79.7486.67 ± 0.2991.83 ± 0.7691.50 ± 0.8750.17 ± 3.2595.17 ± 3.6970.67 ± 6.8372.17 ± 3.69
GAFalse78.2685.50 ± 2.2989.83 ± 2.2588.83 ± 4.6551.67 ± 2.2595.33 ± 2.0268.83 ± 8.7567.83 ± 5.77
\n", - "
" - ], - "text/plain": [ - "task Average agnews \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 79.74 86.67 ± 0.29 \n", - " GA False 78.26 85.50 ± 2.29 \n", - "\n", - "task cr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 91.83 ± 0.76 \n", - " GA False 89.83 ± 2.25 \n", - "\n", - "task mr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 91.50 ± 0.87 \n", - " GA False 88.83 ± 4.65 \n", - "\n", - "task sst-5 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 50.17 ± 3.25 \n", - " GA False 51.67 ± 2.25 \n", - "\n", - "task sst2 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 95.17 ± 3.69 \n", - " GA False 95.33 ± 2.02 \n", - "\n", - "task subj trec \n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B Llama-3-70B DE False 70.67 ± 6.83 72.17 ± 3.69 \n", - " GA False 68.83 ± 8.75 67.83 ± 5.77 " - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_pants = format_score(df_comparison)\n", - "get_result_table(fancy_pants)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Llama-3-8B" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_comparison = read_best_scores(\"experiment\")\n", - "df_comparison = clean_names(df_comparison)\n", - "df_comparison = df_comparison[df_comparison[\"meta_llm\"] == \"Llama-3-8B\"]\n", - "\n", - "df_comparison = get_mean_std(df_comparison)\n", - "df_comparison = get_avg_across_tasks(df_comparison)\n", - "\n", - "plot_bar(df_comparison, hue=\"optimizer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskAverageagnewscrmrsst-5sst2subjtrec
meta_llmdownstream_llmoptimizeruse_task_desc
Llama-3-8BLlama-3-70BDEFalse74.5285.00 ± 1.8091.67 ± 1.1590.67 ± 2.3638.00 ± 14.3192.00 ± 8.6758.00 ± 10.4466.33 ± 16.74
GAFalse74.5086.50 ± 1.5083.00 ± 5.0784.33 ± 5.3051.67 ± 2.3691.33 ± 3.7961.00 ± 7.0063.67 ± 10.05
\n", - "
" - ], - "text/plain": [ - "task Average agnews \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 74.52 85.00 ± 1.80 \n", - " GA False 74.50 86.50 ± 1.50 \n", - "\n", - "task cr mr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 91.67 ± 1.15 90.67 ± 2.36 \n", - " GA False 83.00 ± 5.07 84.33 ± 5.30 \n", - "\n", - "task sst-5 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 38.00 ± 14.31 \n", - " GA False 51.67 ± 2.36 \n", - "\n", - "task sst2 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 92.00 ± 8.67 \n", - " GA False 91.33 ± 3.79 \n", - "\n", - "task subj \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 58.00 ± 10.44 \n", - " GA False 61.00 ± 7.00 \n", - "\n", - "task trec \n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-8B Llama-3-70B DE False 66.33 ± 16.74 \n", - " GA False 63.67 ± 10.05 " - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_pants = format_score(df_comparison)\n", - "get_result_table(fancy_pants)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Experiment (2): GPT-4o vs. initial prompts" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "df_comparison = read_best_scores(\"experiment-initial-prompts\")\n", - "df_comparison[[\"meta_llm\", \"optimizer\", \"use_task_desc\", \"evaluation_llm\"]] = \"init\"\n", - "df_comparison = df_comparison.drop(\"prompt\", axis=\"columns\")\n", - "df_comparison = clean_names(df_comparison)\n", - "df_comparison = df_comparison[df_comparison[\"downstream_llm\"] == \"gpt-4o\"]\n", - "df_comparison = df_comparison[df_comparison[\"task\"].isin(df_experiment.task.unique())]\n", - "df_comparison = get_mean_std(df_comparison)\n", - "df_comparison = get_avg_across_tasks(df_comparison)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_plot = pd.concat([df_experiment, df_comparison])\n", - "plot_bar(df_plot, hue=\"meta_llm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskAverageagnewscrsst-5subj
meta_llmdownstream_llmoptimizeruse_task_desc
Llama-3-70Bgpt-4oDEFalse73.9282.17 ± 0.5892.67 ± 3.3351.33 ± 1.5369.50 ± 2.60
initgpt-4oinitFalse65.7185.50 ± 0.8782.33 ± 20.7137.83 ± 19.4357.17 ± 12.10
\n", - "
" - ], - "text/plain": [ - "task Average agnews \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B gpt-4o DE False 73.92 82.17 ± 0.58 \n", - "init gpt-4o init False 65.71 85.50 ± 0.87 \n", - "\n", - "task cr \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B gpt-4o DE False 92.67 ± 3.33 \n", - "init gpt-4o init False 82.33 ± 20.71 \n", - "\n", - "task sst-5 \\\n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B gpt-4o DE False 51.33 ± 1.53 \n", - "init gpt-4o init False 37.83 ± 19.43 \n", - "\n", - "task subj \n", - "meta_llm downstream_llm optimizer use_task_desc \n", - "Llama-3-70B gpt-4o DE False 69.50 ± 2.60 \n", - "init gpt-4o init False 57.17 ± 12.10 " - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_pants = format_score(df_plot)\n", - "get_result_table(fancy_pants)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "promptolution-BSFNTM07-py3.11", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/interesting_prompts.ipynb b/notebooks/interesting_prompts.ipynb deleted file mode 100644 index 646cceb..0000000 --- a/notebooks/interesting_prompts.ipynb +++ /dev/null @@ -1,2635 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from pathlib import Path\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from typing import List" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [], - "source": [ - "def read_prompts(target_experiment: str, tasks: List[str]):\n", - " results = pd.DataFrame()\n", - " for logging_dir in Path(f\"../logs/{target_experiment}\").rglob(\"*.csv\"):\n", - " if \"best_scores\" in str(logging_dir) or not any(task in str(logging_dir) for task in tasks):\n", - " continue\n", - "\n", - " result = pd.read_csv(logging_dir)\n", - "\n", - " logging_dir = str(logging_dir)\n", - "\n", - " logging_dir = logging_dir.replace(f\"..\\\\logs\\\\{target_experiment}\\\\\", \"\")\n", - " logging_dir = logging_dir.replace(\".csv\", \"\")\n", - "\n", - " task_name, optimizer, meta_llm, evaluation_llm, random_seed = logging_dir.split(\"_\")\n", - "\n", - " metainformation = pd.DataFrame(\n", - " {\n", - " \"task\": [task_name] * len(result),\n", - " \"optimizer\": [optimizer] * len(result),\n", - " \"meta_llm\": [meta_llm] * len(result),\n", - " \"evaluation_llm\": [evaluation_llm] * len(result),\n", - " \"random_seed\": [random_seed] * len(result),\n", - " }\n", - " )\n", - "\n", - " result = pd.concat([result, metainformation], axis=1)\n", - "\n", - " results = pd.concat([result, results], axis=0)\n", - "\n", - " return results\n", - "\n", - "\n", - "def read_best_scores(target_experiment: str):\n", - " return pd.read_csv(f\"../logs/{target_experiment}/best_scores.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [], - "source": [ - "df1 = read_best_scores(\"experiment_eval\")" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = read_best_scores(\"experiment-initial-prompts\")" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.concat([df1, df2])" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [], - "source": [ - "df = df[df[\"downstream_llm\"] == r\"meta-llama/Meta-Llama-3-70B-Instruct\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[df[\"meta_llm\"] == r\"meta-llama\\Meta-Llama-3-70B-Instruct\", \"meta_llm\"] = \"Llama70B\"\n", - "df.loc[df[\"meta_llm\"] == r\"meta-llama\\Meta-Llama-3-8B-Instruct\", \"meta_llm\"] = \"Llama8B\"\n", - "df.loc[df[\"optimizer\"] == \"evopromptde\", \"optimizer\"] = \"DE\"\n", - "df.loc[df[\"optimizer\"] == \"evopromptga\", \"optimizer\"] = \"GA\"" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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meta_llmoptimizerprompttest_score
76Llama8BDELet's follow the instructions step-by-step to generate a better prompt.\\r\\n\\r\\n1. Identify the different parts between Prompt 1 and Prompt 2:\\r\\n\\r\\nPrompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.\\r\\nPrompt 2: You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\".\\r\\n\\r\\nDifferent parts:\\r\\n\\r\\n* \"Your task is to choose\" vs \"You are given a question and need to detect\"\\r\\n* \"a type of the question\" vs \"which category\"\\r\\n* \"Description, Entity, Expression, Human, Location and Number\" vs not changed\\r\\n\\r\\n2. Randomly mutate the different parts:\\r\\n\\r\\n* \"Your task is to choose\" -> \"The goal is to determine\"\\r\\n* \"a type of the question\" -> \"the nature of the inquiry\"\\r\\n* \"Description, Entity, Expression, Human, Location and Number\" -> \"categories: Description, Entity, Expression, Human, Location, and Number Type\"\\r\\n\\r\\n3. Crossover the different parts with Prompt 3 and generate a final prompt:\\r\\n\\r\\nPrompt 3: Identify the category that corresponds to this sentence:0.270
41NaNNaNPlease select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.0.330
73Llama70BDEYou are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number.0.430
77Llama8BDEDetermine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number.0.440
81Llama8BGAAssign the given sentence to one of the six categories (Description, Entity, Expression, Human, Location, or Number) and indicate the corresponding question type.0.515
40NaNNaNDetermine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number.0.595
80Llama70BGACategorize the question into one of the six types - Description, Entity, Expression, Human, Location, or Number - and provide the relevant label.0.620
78Llama70BGAIdentify the most suitable category (Description, Entity, Expression, Human, Location, or Number) for the provided text or question.0.630
83Llama8BGAClassify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information.0.660
74Llama70BDEAs you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input.0.665
39NaNNaNPlease perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text0.670
82Llama8BGAYou are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\".0.685
72Llama70BDEYour task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.0.730
79Llama70BGAYour task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.0.730
75Llama8BDEYour task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.0.755
\n", - "
" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "76 Llama8B DE \n", - "41 NaN NaN \n", - "73 Llama70B DE \n", - "77 Llama8B DE \n", - "81 Llama8B GA \n", - "40 NaN NaN \n", - "80 Llama70B GA \n", - "78 Llama70B GA \n", - "83 Llama8B GA \n", - "74 Llama70B DE \n", - "39 NaN NaN \n", - "82 Llama8B GA \n", - "72 Llama70B DE \n", - "79 Llama70B GA \n", - "75 Llama8B DE \n", - "\n", - " prompt \\\n", - "76 Let's follow the instructions step-by-step to generate a better prompt.\\r\\n\\r\\n1. Identify the different parts between Prompt 1 and Prompt 2:\\r\\n\\r\\nPrompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.\\r\\nPrompt 2: You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\".\\r\\n\\r\\nDifferent parts:\\r\\n\\r\\n* \"Your task is to choose\" vs \"You are given a question and need to detect\"\\r\\n* \"a type of the question\" vs \"which category\"\\r\\n* \"Description, Entity, Expression, Human, Location and Number\" vs not changed\\r\\n\\r\\n2. Randomly mutate the different parts:\\r\\n\\r\\n* \"Your task is to choose\" -> \"The goal is to determine\"\\r\\n* \"a type of the question\" -> \"the nature of the inquiry\"\\r\\n* \"Description, Entity, Expression, Human, Location and Number\" -> \"categories: Description, Entity, Expression, Human, Location, and Number Type\"\\r\\n\\r\\n3. Crossover the different parts with Prompt 3 and generate a final prompt:\\r\\n\\r\\nPrompt 3: Identify the category that corresponds to this sentence: \n", - "41 Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number. \n", - "73 You are required to categorize the given statement into its correct category: Description, Entity, Expression, Human, Location, or Number. \n", - "77 Determine the relevant category for this text based on the categories: Description, Entity, Expression, Human, Location, or Number. \n", - "81 Assign the given sentence to one of the six categories (Description, Entity, Expression, Human, Location, or Number) and indicate the corresponding question type. \n", - "40 Determine the type of the given question and choose from Description, Entity, Expression, Human, Location and Number. \n", - "80 Categorize the question into one of the six types - Description, Entity, Expression, Human, Location, or Number - and provide the relevant label. \n", - "78 Identify the most suitable category (Description, Entity, Expression, Human, Location, or Number) for the provided text or question. \n", - "83 Classify an English question into its corresponding category from the list ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number'] without providing additional information. \n", - "74 As you examine the question, your task is to choose a type, from Description, Entity, Expression, Human, Location, or Number, by analyzing the input. \n", - "39 Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text \n", - "82 You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\". \n", - "72 Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. \n", - "79 Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. \n", - "75 Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. \n", - "\n", - " test_score \n", - "76 0.270 \n", - "41 0.330 \n", - "73 0.430 \n", - "77 0.440 \n", - "81 0.515 \n", - "40 0.595 \n", - "80 0.620 \n", - "78 0.630 \n", - "83 0.660 \n", - "74 0.665 \n", - "39 0.670 \n", - "82 0.685 \n", - "72 0.730 \n", - "79 0.730 \n", - "75 0.755 " - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "df[(df[\"task\"] == \"trec\")].sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please select the correct classification for this sentence: Description, Entity, Expression, Human, Location, or Number.\t=> 0.330\n", - "\n", - "Please perform Question Classification task. Given the question, assign a label from ['Description', 'Entity', 'Expression', 'Human', 'Location', 'Number']. Return label only without any other text\t0.840\n", - "\n", - "OBACHT ICH GLAUBE DAS IS NICHT VON UNS OPTIMIERT SONDERN INIT POP" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TIMO BESONDERS WICHTIG:\n", - "\n", - "Let's follow the instructions step-by-step to generate a better prompt.\\r\\n\\r\\n1. Identify the different parts between Prompt 1 and Prompt 2:\\r\\n\\r\\nPrompt 1: Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.\\r\\nPrompt 2: You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\".\\r\\n\\r\\nDifferent parts:\\r\\n\\r\\n* \"Your task is to choose\" vs \"You are given a question and need to detect\"\\r\\n* \"a type of the question\" vs \"which category\"\\r\\n* \"Description, Entity, Expression, Human, Location and Number\" vs not changed\\r\\n\\r\\n2. Randomly mutate the different parts:\\r\\n\\r\\n* \"Your task is to choose\" -> \"The goal is to determine\"\\r\\n* \"a type of the question\" -> \"the nature of the inquiry\"\\r\\n* \"Description, Entity, Expression, Human, Location and Number\" -> \"categories: Description, Entity, Expression, Human, Location, and Number Type\"\\r\\n\\r\\n3. Crossover the different parts with Prompt 3 and generate a final prompt:\\r\\n\\r\\nPrompt 3: Identify the category that corresponds to this sentence:\t0.270" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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meta_llmoptimizerprompttest_score
36NaNNaNclassify each sentence as either \"objective\" or \"subjective\".0.465
38NaNNaNevaluate each sentence as either objective or subjective.0.545
65Llama8BDEDetermine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction.0.570
70Llama8BGAExaminer, categorize movie reviews as objective or subjective, pinpointing their level of neutrality, and provide a detailed breakdown of each category, highlighting its distinct characteristics.0.590
37NaNNaNYour task is to classify the comment \"subjective\" or \"objective\".0.610
68Llama70BGAevaluate the given sentences and determine whether they are subjective or objective.0.615
66Llama70BGADetermine whether the provided statement is objective, conveying factual information, or subjective, expressing a personal viewpoint or bias.0.680
69Llama8BGADetermine the tone of the input text, classifying it as objective or subjective by identifying and explaining the linguistics features that contribute to its emotional or informative nature.0.685
64Llama8BDEidentify whether the given sentence was expressing an objective or a subjective opinion.0.695
62Llama70BDEConsidering its content, identify the nature of a passage as expressing a subjective or objective opinion from its wording.0.695
71Llama8BGAAnalyze the provided sentences and categorize them as subjective or objective opinions, preserving their nuance and accuracy.0.695
63Llama8BDEAssess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality.0.700
67Llama70BGAClassify the provided text as expressing either a factual or personal viewpoint, accompanied by a thorough justification for your categorization.0.770
60Llama70BDEGiven an expression, you need to judge whether it represents subjective or objective opinion by assessing the context and meaning.0.780
61Llama70BDEAssess the given sentence and determine whether it is into subjective or objective opinion, evaluating the given sentences and their sentiment.0.785
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" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "36 NaN NaN \n", - "38 NaN NaN \n", - "65 Llama8B DE \n", - "70 Llama8B GA \n", - "37 NaN NaN \n", - "68 Llama70B GA \n", - "66 Llama70B GA \n", - "69 Llama8B GA \n", - "64 Llama8B DE \n", - "62 Llama70B DE \n", - "71 Llama8B GA \n", - "63 Llama8B DE \n", - "67 Llama70B GA \n", - "60 Llama70B DE \n", - "61 Llama70B DE \n", - "\n", - " prompt \\\n", - "36 classify each sentence as either \"objective\" or \"subjective\". \n", - "38 evaluate each sentence as either objective or subjective. \n", - "65 Determine whether the given text is expressing a subjective or objective sentiment and assign a label from ['subjective', 'objective'] using the provided instruction. \n", - "70 Examiner, categorize movie reviews as objective or subjective, pinpointing their level of neutrality, and provide a detailed breakdown of each category, highlighting its distinct characteristics. \n", - "37 Your task is to classify the comment \"subjective\" or \"objective\". \n", - "68 evaluate the given sentences and determine whether they are subjective or objective. \n", - "66 Determine whether the provided statement is objective, conveying factual information, or subjective, expressing a personal viewpoint or bias. \n", - "69 Determine the tone of the input text, classifying it as objective or subjective by identifying and explaining the linguistics features that contribute to its emotional or informative nature. \n", - "64 identify whether the given sentence was expressing an objective or a subjective opinion. \n", - "62 Considering its content, identify the nature of a passage as expressing a subjective or objective opinion from its wording. \n", - "71 Analyze the provided sentences and categorize them as subjective or objective opinions, preserving their nuance and accuracy. \n", - "63 Assess the given phrase and categorize it as either topic-neutral or opinion-based, determining whether it falls under attitude or reality. \n", - "67 Classify the provided text as expressing either a factual or personal viewpoint, accompanied by a thorough justification for your categorization. \n", - "60 Given an expression, you need to judge whether it represents subjective or objective opinion by assessing the context and meaning. \n", - "61 Assess the given sentence and determine whether it is into subjective or objective opinion, evaluating the given sentences and their sentiment. \n", - "\n", - " test_score \n", - "36 0.465 \n", - "38 0.545 \n", - "65 0.570 \n", - "70 0.590 \n", - "37 0.610 \n", - "68 0.615 \n", - "66 0.680 \n", - "69 0.685 \n", - "64 0.695 \n", - "62 0.695 \n", - "71 0.695 \n", - "63 0.700 \n", - "67 0.770 \n", - "60 0.780 \n", - "61 0.785 " - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "df[(df[\"task\"] == \"subj\")].sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "classify each sentence as either \"objective\" or \"subjective\".\t0.465\n", - "\n", - "Assess the given sentence and determine whether it is into subjective or objective opinion, evaluating the given sentences and their sentiment.\t0.785" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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meta_llmoptimizerprompttest_score
22Llama8BGADetermine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.0.805
14Llama70BDEAs a sentiment analyzer, evaluate the input0.825
12Llama70BDEAs a sentiment classifier, examine the user review statement and identify the sentiment orientation as either positive or negative, while understanding the meaning and any relevant context.0.855
23Llama8BGAEvaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment.0.870
17Llama8BDEExamine the statement and determine the emotional resonance of the text, evaluating whether it belongs to positive sentiment or a negative opinion.0.885
19Llama70BGAGiven a sentence, classify it as either positive or negative sentiment.0.905
15Llama8BDEGiven a sentence, classify it as either positive or negative sentiment.0.910
21Llama8BGAIdentify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context.0.915
26NaNNaNGiven a tweet, classify it as having a positive or negative sentiment.0.915
20Llama70BGAClassify the sentiment of the provided sentence as either \"positive\" or \"negative\".0.925
25NaNNaNYour task is to classify the comment \"positive\" or \"negative\".0.930
18Llama70BGALabel the provided sentence with either \"positive\" or \"negative\" sentiment.0.945
24NaNNaNPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.0.950
16Llama8BDEPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.0.950
13Llama70BDEPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.0.955
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" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "22 Llama8B GA \n", - "14 Llama70B DE \n", - "12 Llama70B DE \n", - "23 Llama8B GA \n", - "17 Llama8B DE \n", - "19 Llama70B GA \n", - "15 Llama8B DE \n", - "21 Llama8B GA \n", - "26 NaN NaN \n", - "20 Llama70B GA \n", - "25 NaN NaN \n", - "18 Llama70B GA \n", - "24 NaN NaN \n", - "16 Llama8B DE \n", - "13 Llama70B DE \n", - "\n", - " prompt \\\n", - "22 Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral. \n", - "14 As a sentiment analyzer, evaluate the input \n", - "12 As a sentiment classifier, examine the user review statement and identify the sentiment orientation as either positive or negative, while understanding the meaning and any relevant context. \n", - "23 Evaluate the sentiment of the provided statement and categorize it as either a 'positive' or 'negative' sentiment. \n", - "17 Examine the statement and determine the emotional resonance of the text, evaluating whether it belongs to positive sentiment or a negative opinion. \n", - "19 Given a sentence, classify it as either positive or negative sentiment. \n", - "15 Given a sentence, classify it as either positive or negative sentiment. \n", - "21 Identify the sentiment of the input text and determine its emotional tone as either 'positive' or 'negative', taking into account the nuances of the text and its context. \n", - "26 Given a tweet, classify it as having a positive or negative sentiment. \n", - "20 Classify the sentiment of the provided sentence as either \"positive\" or \"negative\". \n", - "25 Your task is to classify the comment \"positive\" or \"negative\". \n", - "18 Label the provided sentence with either \"positive\" or \"negative\" sentiment. \n", - "24 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "16 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "13 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "\n", - " test_score \n", - "22 0.805 \n", - "14 0.825 \n", - "12 0.855 \n", - "23 0.870 \n", - "17 0.885 \n", - "19 0.905 \n", - "15 0.910 \n", - "21 0.915 \n", - "26 0.915 \n", - "20 0.925 \n", - "25 0.930 \n", - "18 0.945 \n", - "24 0.950 \n", - "16 0.950 \n", - "13 0.955 " - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "df[(df[\"task\"] == \"cr\")].sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Determine the sentiment of the input text, categorizing it as positive, negative, optimistic, pessimistic, or neutral.\t0.585\n", - "\n", - "Label the provided sentence with either \"positive\" or \"negative\" sentiment.\t0.945" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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meta_llmoptimizerprompttest_score
7Llama70BGADetermine the primary theme of a given news article and classify it under one of the four categories: World, Sports, Tech, or Business.0.845
8Llama70BGADetermine the most suitable category (World, Sports, Business, or Tech) for a news article based on its dominant subject matter.0.845
23NaNNaNGive the main topic of the news article and then choose from World, Sports, Tech and Business.0.845
3Llama8BDEChoose a word from World, Sports, Business and Tech to categorize the given text.0.850
6Llama70BGADetermine the primary subject of the provided news article and classify it under one of the following categories: World, Sports, Business, or Tech.0.855
2Llama70BDEThe goal is to identify the journal article according to its primary theme and determine whether it belongs to the World, Sports, Business, or Tech category.</prompt0.855
5Llama8BDEGive the main topic of the news article and then choose from World, Sports, Tech and Business.0.855
4Llama8BDEClassify the media report into one of the below-listed sections: World, Sports, Business, or Tech, considering the main topic.0.860
22NaNNaNChoose a word from World, Sports, Business and Tech to categorize the given text.0.870
1Llama70BDEYour task is to identify the subject of the news piece and classify it into one of four categories: World, Sports, Business and Tech.0.875
11Llama8BGAClassify the given news article into one of four categories (World, Sports, Business, or Tech) based on its main theme or subject matter.0.880
10Llama8BGAClassify a news article into one of four themed categories - World, Sports, Business, or Tech - based on its primary focus and emphasis.0.885
21NaNNaNYour responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.0.885
9Llama8BGAYour responsibility is to assign a news article to one of four categories: World, Sports, Business, or Tech, based on its main idea.0.885
0Llama70BDEClassify the topic of the following news as \"World\", \"Sports\", \"Tech\" or \"Business\".0.890
\n", - "
" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "7 Llama70B GA \n", - "8 Llama70B GA \n", - "23 NaN NaN \n", - "3 Llama8B DE \n", - "6 Llama70B GA \n", - "2 Llama70B DE \n", - "5 Llama8B DE \n", - "4 Llama8B DE \n", - "22 NaN NaN \n", - "1 Llama70B DE \n", - "11 Llama8B GA \n", - "10 Llama8B GA \n", - "21 NaN NaN \n", - "9 Llama8B GA \n", - "0 Llama70B DE \n", - "\n", - " prompt \\\n", - "7 Determine the primary theme of a given news article and classify it under one of the four categories: World, Sports, Tech, or Business. \n", - "8 Determine the most suitable category (World, Sports, Business, or Tech) for a news article based on its dominant subject matter. \n", - "23 Give the main topic of the news article and then choose from World, Sports, Tech and Business. \n", - "3 Choose a word from World, Sports, Business and Tech to categorize the given text. \n", - "6 Determine the primary subject of the provided news article and classify it under one of the following categories: World, Sports, Business, or Tech. \n", - "2 The goal is to identify the journal article according to its primary theme and determine whether it belongs to the World, Sports, Business, or Tech category.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
meta_llmoptimizerprompttest_score
28Llama8BDEAnalyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.0.770
34Llama8BGADetermine the sentiment of the input, classifying it as positive or negative emotional tone.0.785
31Llama70BGAClassify the sentiment of the provided sentence or review as either \"positive\" or \"negative\", indicating the attitude towards the subject.0.835
29NaNNaNGiven a tweet, classify it as having a positive or negative sentiment.0.845
33Llama8BGAAssess the given text and categorize it as either a positive or negative sentiment.0.850
29Llama8BDEGiven a statement, classify it as expressing a positive or negative opinion.0.885
24Llama70BDEYour task is to classify the comment \"positive\" or \"negative\".0.895
25Llama70BDEGiven an online message, your task is to classify it as expressing a \"positive\" or \"negative\" opinion, considering whether it is written with a favorable or unfavorable attitude.0.915
32Llama70BGAPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text0.915
35Llama8BGAPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text0.920
28NaNNaNYour task is to classify the comment \"positive\" or \"negative\".0.920
26Llama70BDEPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text0.925
30Llama70BGAYour task is to classify the comment \"positive\" or \"negative\".0.925
27Llama8BDEYour task is to classify the comment \"positive\" or \"negative\".0.925
27NaNNaNPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text0.925
\n", - "" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "28 Llama8B DE \n", - "34 Llama8B GA \n", - "31 Llama70B GA \n", - "29 NaN NaN \n", - "33 Llama8B GA \n", - "29 Llama8B DE \n", - "24 Llama70B DE \n", - "25 Llama70B DE \n", - "32 Llama70B GA \n", - "35 Llama8B GA \n", - "28 NaN NaN \n", - "26 Llama70B DE \n", - "30 Llama70B GA \n", - "27 Llama8B DE \n", - "27 NaN NaN \n", - "\n", - " prompt \\\n", - "28 Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier. \n", - "34 Determine the sentiment of the input, classifying it as positive or negative emotional tone. \n", - "31 Classify the sentiment of the provided sentence or review as either \"positive\" or \"negative\", indicating the attitude towards the subject. \n", - "29 Given a tweet, classify it as having a positive or negative sentiment. \n", - "33 Assess the given text and categorize it as either a positive or negative sentiment. \n", - "29 Given a statement, classify it as expressing a positive or negative opinion. \n", - "24 Your task is to classify the comment \"positive\" or \"negative\". \n", - "25 Given an online message, your task is to classify it as expressing a \"positive\" or \"negative\" opinion, considering whether it is written with a favorable or unfavorable attitude. \n", - "32 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text \n", - "35 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text \n", - "28 Your task is to classify the comment \"positive\" or \"negative\". \n", - "26 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text \n", - "30 Your task is to classify the comment \"positive\" or \"negative\". \n", - "27 Your task is to classify the comment \"positive\" or \"negative\". \n", - "27 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text \n", - "\n", - " test_score \n", - "28 0.770 \n", - "34 0.785 \n", - "31 0.835 \n", - "29 0.845 \n", - "33 0.850 \n", - "29 0.885 \n", - "24 0.895 \n", - "25 0.915 \n", - "32 0.915 \n", - "35 0.920 \n", - "28 0.920 \n", - "26 0.925 \n", - "30 0.925 \n", - "27 0.925 \n", - "27 0.925 " - ] - }, - "execution_count": 157, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "df[(df[\"task\"] == \"mr\")].sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Analyze the phrase and categorize its emotional tone into one of the following labels: positive or negative as a sentiment classifier.\t0.770\n", - "\n", - "Your task is to classify the comment \"positive\" or \"negative\".\t0.925" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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meta_llmoptimizerprompttest_score
32NaNNaNYour objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.0.040
39Llama8BDEAnalyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie.0.350
40Llama8BDEAnalyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context.0.365
41Llama8BDEEvaluate the text provided and categorize movie reviews into one of the following categories: terrible, bad, okay, good, or great.0.470
45Llama8BGAEvaluate the emotional tone of the text and categorize it into one of five sentiment categories (terrible, bad, okay, good, or great) based on the presence of positive and negative sentiments, providing a precise and nuanced classification.0.500
37Llama70BDEClassify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great.0.505
47Llama8BGAIdentify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label.0.520
31NaNNaNIn this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great.0.535
36Llama70BDEBased on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great.0.560
43Llama70BGAAssign a sentiment label ('terrible', 'bad', 'okay', 'good', or 'great') to the provided movie review, reflecting the overall emotional tone of the text.0.560
38Llama70BDEAnalyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great.0.560
44Llama70BGARate the emotional tone of the provided text as terrible, bad, okay, good, or great, reflecting the overall sentiment expressed.0.570
42Llama70BGADetermine the sentiment of the given text by labeling it as 'terrible', 'bad', 'okay', 'good', or 'great' according to the author's expressed emotions.0.605
46Llama8BGAClassify the provided comment according to its sentiment intensity, assigning a label from ['terrible', 'bad', 'okay', 'good', 'great'] without providing additional context.0.615
30NaNNaNPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text.0.620
\n", - "
" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "32 NaN NaN \n", - "39 Llama8B DE \n", - "40 Llama8B DE \n", - "41 Llama8B DE \n", - "45 Llama8B GA \n", - "37 Llama70B DE \n", - "47 Llama8B GA \n", - "31 NaN NaN \n", - "36 Llama70B DE \n", - "43 Llama70B GA \n", - "38 Llama70B DE \n", - "44 Llama70B GA \n", - "42 Llama70B GA \n", - "46 Llama8B GA \n", - "30 NaN NaN \n", - "\n", - " prompt \\\n", - "32 Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great. \n", - "39 Analyze the movie criticism provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great, while considering the context and tone of the movie. \n", - "40 Analyze the given text and assign it to one of the following categories: terrible, bad, okay, good, or great, considering the relevant context. \n", - "41 Evaluate the text provided and categorize movie reviews into one of the following categories: terrible, bad, okay, good, or great. \n", - "45 Evaluate the emotional tone of the text and categorize it into one of five sentiment categories (terrible, bad, okay, good, or great) based on the presence of positive and negative sentiments, providing a precise and nuanced classification. \n", - "37 Classify the movie review provided to you into one of five categories based on the sentiment: terrible, bad, okay, good, or great. \n", - "47 Identify the sentiment of the given text and categorize it as 'terrible', 'bad', 'okay', 'good', or 'great' based on its tone and language, outputting the corresponding sentiment label. \n", - "31 In this task, you are given movie reviews. Based on it, classify it to one of the five classes: (1) terrible, (2) bad, (3) okay, (4) good, and (5) great. \n", - "36 Based on the given movie review, rate it into one of five ratings based on the sentiment: terrible, bad, okay, good, or great. \n", - "43 Assign a sentiment label ('terrible', 'bad', 'okay', 'good', or 'great') to the provided movie review, reflecting the overall emotional tone of the text. \n", - "38 Analyze the sentence and categorize it into one of five categories based on the sentiment: terrible, bad, okay, good, or great. \n", - "44 Rate the emotional tone of the provided text as terrible, bad, okay, good, or great, reflecting the overall sentiment expressed. \n", - "42 Determine the sentiment of the given text by labeling it as 'terrible', 'bad', 'okay', 'good', or 'great' according to the author's expressed emotions. \n", - "46 Classify the provided comment according to its sentiment intensity, assigning a label from ['terrible', 'bad', 'okay', 'good', 'great'] without providing additional context. \n", - "30 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['terrible', 'bad', 'okay', 'good', 'great']. Return label only without any other text. \n", - "\n", - " test_score \n", - "32 0.040 \n", - "39 0.350 \n", - "40 0.365 \n", - "41 0.470 \n", - "45 0.500 \n", - "37 0.505 \n", - "47 0.520 \n", - "31 0.535 \n", - "36 0.560 \n", - "43 0.560 \n", - "38 0.560 \n", - "44 0.570 \n", - "42 0.605 \n", - "46 0.615 \n", - "30 0.620 " - ] - }, - "execution_count": 158, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "df[(df[\"task\"] == \"sst-5\")].sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your objective is to analyze the movie review and allocate it to one of five categories, from terrible to great.\t0.040\n", - "\n", - "Classify the provided comment according to its sentiment intensity, assigning a label from ['terrible', 'bad', 'okay', 'good', 'great'] without providing additional context.\t0.615\n", - "\n", - "DESWEGEN HOHE STDABW!" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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meta_llmoptimizerprompttest_score
50Llama70BDEYou will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.0.835
59Llama8BGADetermine the emotional tone of the given text by deciphering its meaning and context, and categorize it as either positive or negative sentiment.0.855
35NaNNaNGiven a tweet, classify it as having a positive or negative sentiment.0.890
49Llama70BDEGiven a sentence, classify it as either positive or negative sentiment.0.895
58Llama8BGAEvaluate the emotional tone and sentiment of the provided text, categorizing its emotional connotation as 'strongly positive', 'positive', 'neutral', 'negative', or 'strongly negative', and provide a nuanced intensity level if needed, or 'positive' or 'negative' if the sentiment is straightforward.0.905
56Llama70BGAIdentify the emotional tone of the text, categorizing it as either \"positive\" or \"negative\" sentiment.0.910
53Llama8BDEAnalyze a review and classify it as expressing a positive or negative opinion.0.910
57Llama8BGAGiven a tweet, classify it as having a positive or negative sentiment.0.915
48Llama70BDEYour task is to classify the comment \"positive\" or \"negative\".0.945
34NaNNaNYour task is to classify the comment \"positive\" or \"negative\".0.945
52Llama8BDEExamine the review and classify it as having a positive or negative sentiment, while considering the tone and context.0.950
51Llama8BDEYour task is to classify the comment \"positive\" or \"negative\".0.950
55Llama70BGAYour task is to classify the comment \"positive\" or \"negative\".0.950
54Llama70BGAYour task is to classify the comment \"positive\" or \"negative\".0.950
33NaNNaNPlease perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.0.960
\n", - "
" - ], - "text/plain": [ - " meta_llm optimizer \\\n", - "50 Llama70B DE \n", - "59 Llama8B GA \n", - "35 NaN NaN \n", - "49 Llama70B DE \n", - "58 Llama8B GA \n", - "56 Llama70B GA \n", - "53 Llama8B DE \n", - "57 Llama8B GA \n", - "48 Llama70B DE \n", - "34 NaN NaN \n", - "52 Llama8B DE \n", - "51 Llama8B DE \n", - "55 Llama70B GA \n", - "54 Llama70B GA \n", - "33 NaN NaN \n", - "\n", - " prompt \\\n", - "50 You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion. \n", - "59 Determine the emotional tone of the given text by deciphering its meaning and context, and categorize it as either positive or negative sentiment. \n", - "35 Given a tweet, classify it as having a positive or negative sentiment. \n", - "49 Given a sentence, classify it as either positive or negative sentiment. \n", - "58 Evaluate the emotional tone and sentiment of the provided text, categorizing its emotional connotation as 'strongly positive', 'positive', 'neutral', 'negative', or 'strongly negative', and provide a nuanced intensity level if needed, or 'positive' or 'negative' if the sentiment is straightforward. \n", - "56 Identify the emotional tone of the text, categorizing it as either \"positive\" or \"negative\" sentiment. \n", - "53 Analyze a review and classify it as expressing a positive or negative opinion. \n", - "57 Given a tweet, classify it as having a positive or negative sentiment. \n", - "48 Your task is to classify the comment \"positive\" or \"negative\". \n", - "34 Your task is to classify the comment \"positive\" or \"negative\". \n", - "52 Examine the review and classify it as having a positive or negative sentiment, while considering the tone and context. \n", - "51 Your task is to classify the comment \"positive\" or \"negative\". \n", - "55 Your task is to classify the comment \"positive\" or \"negative\". \n", - "54 Your task is to classify the comment \"positive\" or \"negative\". \n", - "33 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "\n", - " test_score \n", - "50 0.835 \n", - "59 0.855 \n", - "35 0.890 \n", - "49 0.895 \n", - "58 0.905 \n", - "56 0.910 \n", - "53 0.910 \n", - "57 0.915 \n", - "48 0.945 \n", - "34 0.945 \n", - "52 0.950 \n", - "51 0.950 \n", - "55 0.950 \n", - "54 0.950 \n", - "33 0.960 " - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "df[(df[\"task\"] == \"sst2\")].sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will be responsible for evaluating the emotional tone in the input message and classify it as expressing a positive or negative opinion.\t0.835\n", - "\n", - "Examine the review and classify it as having a positive or negative sentiment, while considering the tone and context.\t0.950" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['agnews', 'cr', 'mr', 'sst-5', 'sst2', 'subj', 'trec'],\n", - " dtype=object)" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[\"task\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " meta_llm optimizer \\\n", - "3 Llama8B GA \n", - "5 Llama8B GA \n", - "4 Llama8B GA \n", - "2 Llama8B DE \n", - "0 Llama8B DE \n", - "1 Llama8B DE \n", - "\n", - " prompt \\\n", - "3 Choose a word from World, Sports, Business and Tech to categorize the given text. \n", - "5 Classify the given news article into one of the four main categories: World, Sports, Business, or Tech, based on the article's topic. \n", - "4 Classify the given news article into one of the four categories ['World', 'Sports', 'Business', or 'Tech'] based on its primary theme and main topic, ensuring accurate categorization. \n", - "2 The objective is to assign a news article to one of the following categories: World, Sports, Business, or Tech, based on its main topic. \n", - "0 You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic. \n", - "1 Your task is to classify the news item as \"World\", \"Sports\", \"Tech\" or \"Business\". \n", - "\n", - " test_score \n", - "3 0.830 \n", - "5 0.870 \n", - "4 0.880 \n", - "2 0.880 \n", - "0 0.885 \n", - "1 0.890 \n", - " meta_llm optimizer \\\n", - "6 Llama8B DE \n", - "7 Llama8B DE \n", - "8 Llama8B DE \n", - "10 Llama8B GA \n", - "11 Llama8B GA \n", - "9 Llama8B GA \n", - "\n", - " prompt \\\n", - "6 Consider customer reviews and analyze them to determine their emotional tone, classifying them as expressing either positive or negative sentiment. \n", - "7 As a sentiment classifier, examine the text passage for sentiment in customer reviews, by assessing the overall emotional direction, and classify the expression as either positive or negative. \n", - "8 You will be tasked with analyzing text to determine its emotional tone, identifying whether it expresses a positive or negative sentiment, while considering the broader context. \n", - "10 Classify the provided text as having positive or negative sentiment, determining the corresponding sentiment label from ['negative', 'positive']. \n", - "11 Given a review, classify it as expressing a positive or negative sentiment. \n", - "9 Classify this customer review as expressing either a \"positive\" or \"negative\" sentiment, analyzing its tone and content. \n", - "\n", - " test_score \n", - "6 0.785 \n", - "7 0.855 \n", - "8 0.855 \n", - "10 0.930 \n", - "11 0.930 \n", - "9 0.940 \n", - " meta_llm optimizer \\\n", - "13 Llama8B DE \n", - "17 Llama8B GA \n", - "12 Llama8B DE \n", - "14 Llama8B DE \n", - "16 Llama8B GA \n", - "15 Llama8B GA \n", - "\n", - " prompt \\\n", - "13 To categorize the sentiment of text snippets in a movie review, classify the sentiment as either 'negative' or 'positive', taking into consideration \n", - "17 Classify the movie review text, determining whether it expresses a positive or negative sentiment, and output the corresponding label ('positive' or 'negative') \n", - "12 Given a tweet, classify it as having a positive or negative sentiment. \n", - "14 Given a tweet, classify it as having a positive or negative sentiment. \n", - "16 Based on the provided movie review, please classify its sentiment as either \"positive\" or \"negative\" according to the given binary sentiment annotations. \n", - "15 Classify a movie review as expressing either 'negative' or 'positive' sentiment by identifying the underlying emotion. \n", - "\n", - " test_score \n", - "13 0.760 \n", - "17 0.850 \n", - "12 0.885 \n", - "14 0.890 \n", - "16 0.905 \n", - "15 0.915 \n", - " meta_llm optimizer \\\n", - "20 Llama8B DE \n", - "18 Llama8B DE \n", - "23 Llama8B GA \n", - "22 Llama8B GA \n", - "21 Llama8B GA \n", - "19 Llama8B DE \n", - "\n", - " prompt \\\n", - "20 Analyze movie reviews and classify them into one of the following categories: terrible, bad, okay, good, or great. \n", - "18 Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment. \n", - "23 Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list. \n", - "22 Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good \n", - "21 Classify the sentiment of the given movie review among ['terrible', 'bad', 'okay', 'good', 'great'] based on its emotional tone, leveraging your language understanding \n", - "19 The object is to classify movie reviews into one of the following categories: terrible, bad, okay, good, or great, based on the sentiment. \n", - "\n", - " test_score \n", - "20 0.400 \n", - "18 0.450 \n", - "23 0.470 \n", - "22 0.490 \n", - "21 0.565 \n", - "19 0.570 \n", - " meta_llm optimizer \\\n", - "25 Llama8B DE \n", - "26 Llama8B DE \n", - "29 Llama8B GA \n", - "28 Llama8B GA \n", - "24 Llama8B DE \n", - "27 Llama8B GA \n", - "\n", - " prompt \\\n", - "25 As a sentiment classifier, consider a movie review sentence and analyze its emotional tone, classifying it as either positive or negative sentiment, while taking into account its meaning and context. \n", - "26 Examine the written opinion in a movie review and classify it as either \"positive\" or \"negative\", considering the sentence meaning and relevant context. \n", - "29 Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label. \n", - "28 Determine whether the given movie review expresses strongly positive or strongly negative sentiment. \n", - "24 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "27 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "\n", - " test_score \n", - "25 0.815 \n", - "26 0.880 \n", - "29 0.910 \n", - "28 0.935 \n", - "24 0.940 \n", - "27 0.945 \n", - " meta_llm optimizer \\\n", - "31 Llama8B DE \n", - "32 Llama8B DE \n", - "34 Llama8B GA \n", - "33 Llama8B GA \n", - "30 Llama8B DE \n", - "35 Llama8B GA \n", - "\n", - " prompt \\\n", - "31 determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective. \n", - "32 Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context \n", - "34 Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics. \n", - "33 Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues. \n", - "30 Your task is to examine sentences from movie reviews and understand the purpose of the utterance, and then determine the intention behind the statement, by classifying them as either subjective or objective \n", - "35 Given a statement, classify it as expressing a subjective or objective opinion. \n", - "\n", - " test_score \n", - "31 0.585 \n", - "32 0.630 \n", - "34 0.695 \n", - "33 0.705 \n", - "30 0.750 \n", - "35 0.755 \n", - " meta_llm optimizer \\\n", - "39 Llama8B GA \n", - "41 Llama8B GA \n", - "36 Llama8B DE \n", - "37 Llama8B DE \n", - "40 Llama8B GA \n", - "38 Llama8B DE \n", - "\n", - " prompt \\\n", - "39 Please classify each question into its primary category from the following options: Description, Entity, Expression, Human, Location, Number. \n", - "41 Recognize the primary category of each question, choosing from Description, Entity, Expression, Human, Location, or Number, and return the most fitting label. \n", - "36 Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context. \n", - "37 You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\". \n", - "40 For each question, select the primary category (Description, Entity, Expression, Human, Location, or Number). \n", - "38 Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. \n", - "\n", - " test_score \n", - "39 0.605 \n", - "41 0.615 \n", - "36 0.630 \n", - "37 0.645 \n", - "40 0.685 \n", - "38 0.750 \n" - ] - } - ], - "source": [ - "df1 = read_best_scores(\"experiment_eval_task_descr\")\n", - "\n", - "for task in df1[\"task\"].unique():\n", - " df = df1[df1[\"task\"] == task]\n", - "\n", - " df = df[df[\"downstream_llm\"] == r\"meta-llama/Meta-Llama-3-70B-Instruct\"]\n", - "\n", - " df.loc[df[\"meta_llm\"] == r\"meta-llama\\Meta-Llama-3-70B-Instruct\", \"meta_llm\"] = \"Llama70B\"\n", - " df.loc[df[\"meta_llm\"] == r\"meta-llama\\Meta-Llama-3-8B-Instruct\", \"meta_llm\"] = \"Llama8B\"\n", - " df.loc[df[\"optimizer\"] == \"evopromptde\", \"optimizer\"] = \"DE\"\n", - " df.loc[df[\"optimizer\"] == \"evopromptga\", \"optimizer\"] = \"GA\"\n", - "\n", - " print(df.sort_values(\"test_score\")[[\"meta_llm\", \"optimizer\", \"prompt\", \"test_score\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskoptimizermeta_llmdownstream_llmevaluation_llmrandom_seedprompttrain_scoretest_score
0agnewsevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic.0.950.885
1agnewsevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Your task is to classify the news item as \"World\", \"Sports\", \"Tech\" or \"Business\".0.900.890
2agnewsevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69The objective is to assign a news article to one of the following categories: World, Sports, Business, or Tech, based on its main topic.1.000.880
3agnewsevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Choose a word from World, Sports, Business and Tech to categorize the given text.1.000.830
4agnewsevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Classify the given news article into one of the four categories ['World', 'Sports', 'Business', or 'Tech'] based on its primary theme and main topic, ensuring accurate categorization.1.000.880
5agnewsevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Classify the given news article into one of the four main categories: World, Sports, Business, or Tech, based on the article's topic.0.950.870
6crevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Consider customer reviews and analyze them to determine their emotional tone, classifying them as expressing either positive or negative sentiment.0.950.785
7crevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47As a sentiment classifier, examine the text passage for sentiment in customer reviews, by assessing the overall emotional direction, and classify the expression as either positive or negative.0.950.855
8crevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69You will be tasked with analyzing text to determine its emotional tone, identifying whether it expresses a positive or negative sentiment, while considering the broader context.1.000.855
9crevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Classify this customer review as expressing either a \"positive\" or \"negative\" sentiment, analyzing its tone and content.1.000.940
10crevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Classify the provided text as having positive or negative sentiment, determining the corresponding sentiment label from ['negative', 'positive'].1.000.930
11crevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Given a review, classify it as expressing a positive or negative sentiment.0.950.930
12mrevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Given a tweet, classify it as having a positive or negative sentiment.0.950.885
13mrevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47To categorize the sentiment of text snippets in a movie review, classify the sentiment as either 'negative' or 'positive', taking into consideration0.950.760
14mrevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Given a tweet, classify it as having a positive or negative sentiment.0.950.890
15mrevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Classify a movie review as expressing either 'negative' or 'positive' sentiment by identifying the underlying emotion.1.000.915
16mrevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Based on the provided movie review, please classify its sentiment as either \"positive\" or \"negative\" according to the given binary sentiment annotations.1.000.905
17mrevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Classify the movie review text, determining whether it expresses a positive or negative sentiment, and output the corresponding label ('positive' or 'negative')1.000.850
18sst-5evopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment.0.500.450
19sst-5evopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47The object is to classify movie reviews into one of the following categories: terrible, bad, okay, good, or great, based on the sentiment.0.650.570
20sst-5evopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Analyze movie reviews and classify them into one of the following categories: terrible, bad, okay, good, or great.0.550.400
21sst-5evopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Classify the sentiment of the given movie review among ['terrible', 'bad', 'okay', 'good', 'great'] based on its emotional tone, leveraging your language understanding0.650.565
22sst-5evopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good0.650.490
23sst-5evopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list.0.600.470
24sst2evopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.1.000.940
25sst2evopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47As a sentiment classifier, consider a movie review sentence and analyze its emotional tone, classifying it as either positive or negative sentiment, while taking into account its meaning and context.1.000.815
26sst2evopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Examine the written opinion in a movie review and classify it as either \"positive\" or \"negative\", considering the sentence meaning and relevant context.1.000.880
27sst2evopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text.1.000.945
28sst2evopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Determine whether the given movie review expresses strongly positive or strongly negative sentiment.1.000.935
29sst2evopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label.1.000.910
30subjevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Your task is to examine sentences from movie reviews and understand the purpose of the utterance, and then determine the intention behind the statement, by classifying them as either subjective or objective0.800.750
31subjevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective.0.750.585
32subjevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context0.850.630
33subjevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues.0.800.705
34subjevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics.0.750.695
35subjevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Given a statement, classify it as expressing a subjective or objective opinion.0.800.755
36trecevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context.0.550.630
37trecevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\".0.550.645
38trecevopromptdemeta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number.0.450.750
39trecevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct42Please classify each question into its primary category from the following options: Description, Entity, Expression, Human, Location, Number.0.650.605
40trecevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct47For each question, select the primary category (Description, Entity, Expression, Human, Location, or Number).0.550.685
41trecevopromptgameta-llama\\Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-70B-Instructmeta-llama\\Meta-Llama-3-8B-Instruct69Recognize the primary category of each question, choosing from Description, Entity, Expression, Human, Location, or Number, and return the most fitting label.0.700.615
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" - ], - "text/plain": [ - " task optimizer meta_llm \\\n", - "0 agnews evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "1 agnews evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "2 agnews evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "3 agnews evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "4 agnews evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "5 agnews evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "6 cr evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "7 cr evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "8 cr evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "9 cr evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "10 cr evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "11 cr evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "12 mr evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "13 mr evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "14 mr evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "15 mr evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "16 mr evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "17 mr evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "18 sst-5 evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "19 sst-5 evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "20 sst-5 evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "21 sst-5 evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "22 sst-5 evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "23 sst-5 evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "24 sst2 evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "25 sst2 evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "26 sst2 evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "27 sst2 evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "28 sst2 evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "29 sst2 evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "30 subj evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "31 subj evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "32 subj evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "33 subj evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "34 subj evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "35 subj evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "36 trec evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "37 trec evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "38 trec evopromptde meta-llama\\Meta-Llama-3-8B-Instruct \n", - "39 trec evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "40 trec evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "41 trec evopromptga meta-llama\\Meta-Llama-3-8B-Instruct \n", - "\n", - " downstream_llm evaluation_llm \\\n", - "0 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "1 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "2 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "3 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "4 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "5 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "6 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "7 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "8 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "9 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "10 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "11 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "12 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "13 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "14 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "15 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "16 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "17 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "18 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "19 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "20 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "21 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "22 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "23 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "24 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "25 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "26 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "27 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "28 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "29 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "30 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "31 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "32 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "33 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "34 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "35 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "36 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "37 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "38 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "39 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "40 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "41 meta-llama/Meta-Llama-3-70B-Instruct meta-llama\\Meta-Llama-3-8B-Instruct \n", - "\n", - " random_seed \\\n", - "0 42 \n", - "1 47 \n", - "2 69 \n", - "3 42 \n", - "4 47 \n", - "5 69 \n", - "6 42 \n", - "7 47 \n", - "8 69 \n", - "9 42 \n", - "10 47 \n", - "11 69 \n", - "12 42 \n", - "13 47 \n", - "14 69 \n", - "15 42 \n", - "16 47 \n", - "17 69 \n", - "18 42 \n", - "19 47 \n", - "20 69 \n", - "21 42 \n", - "22 47 \n", - "23 69 \n", - "24 42 \n", - "25 47 \n", - "26 69 \n", - "27 42 \n", - "28 47 \n", - "29 69 \n", - "30 42 \n", - "31 47 \n", - "32 69 \n", - "33 42 \n", - "34 47 \n", - "35 69 \n", - "36 42 \n", - "37 47 \n", - "38 69 \n", - "39 42 \n", - "40 47 \n", - "41 69 \n", - "\n", - " prompt \\\n", - "0 You will be given a news article and asked to classify it as World, Sports, Business and Tech, depending on its main topic. \n", - "1 Your task is to classify the news item as \"World\", \"Sports\", \"Tech\" or \"Business\". \n", - "2 The objective is to assign a news article to one of the following categories: World, Sports, Business, or Tech, based on its main topic. \n", - "3 Choose a word from World, Sports, Business and Tech to categorize the given text. \n", - "4 Classify the given news article into one of the four categories ['World', 'Sports', 'Business', or 'Tech'] based on its primary theme and main topic, ensuring accurate categorization. \n", - "5 Classify the given news article into one of the four main categories: World, Sports, Business, or Tech, based on the article's topic. \n", - "6 Consider customer reviews and analyze them to determine their emotional tone, classifying them as expressing either positive or negative sentiment. \n", - "7 As a sentiment classifier, examine the text passage for sentiment in customer reviews, by assessing the overall emotional direction, and classify the expression as either positive or negative. \n", - "8 You will be tasked with analyzing text to determine its emotional tone, identifying whether it expresses a positive or negative sentiment, while considering the broader context. \n", - "9 Classify this customer review as expressing either a \"positive\" or \"negative\" sentiment, analyzing its tone and content. \n", - "10 Classify the provided text as having positive or negative sentiment, determining the corresponding sentiment label from ['negative', 'positive']. \n", - "11 Given a review, classify it as expressing a positive or negative sentiment. \n", - "12 Given a tweet, classify it as having a positive or negative sentiment. \n", - "13 To categorize the sentiment of text snippets in a movie review, classify the sentiment as either 'negative' or 'positive', taking into consideration \n", - "14 Given a tweet, classify it as having a positive or negative sentiment. \n", - "15 Classify a movie review as expressing either 'negative' or 'positive' sentiment by identifying the underlying emotion. \n", - "16 Based on the provided movie review, please classify its sentiment as either \"positive\" or \"negative\" according to the given binary sentiment annotations. \n", - "17 Classify the movie review text, determining whether it expresses a positive or negative sentiment, and output the corresponding label ('positive' or 'negative') \n", - "18 Examine the movie critique and allocate it to one of the following categories: terrible, bad, okay, good, great, while considering the sentiment. \n", - "19 The object is to classify movie reviews into one of the following categories: terrible, bad, okay, good, or great, based on the sentiment. \n", - "20 Analyze movie reviews and classify them into one of the following categories: terrible, bad, okay, good, or great. \n", - "21 Classify the sentiment of the given movie review among ['terrible', 'bad', 'okay', 'good', 'great'] based on its emotional tone, leveraging your language understanding \n", - "22 Classify the text corresponding to a movie review into one of the following five sentiment categories: terrible, bad, okay, good \n", - "23 Transform the given movie review to one of the following sentiment categories: terrible, bad, okay, good, or great, accurately capturing its essence. Return a corresponding sentiment label from the list. \n", - "24 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "25 As a sentiment classifier, consider a movie review sentence and analyze its emotional tone, classifying it as either positive or negative sentiment, while taking into account its meaning and context. \n", - "26 Examine the written opinion in a movie review and classify it as either \"positive\" or \"negative\", considering the sentence meaning and relevant context. \n", - "27 Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from ['negative', 'positive']. Return label only without any other text. \n", - "28 Determine whether the given movie review expresses strongly positive or strongly negative sentiment. \n", - "29 Given a movie review, use context and sentiment cues to predict whether it's 'positive' or 'negative', and return the corresponding label. \n", - "30 Your task is to examine sentences from movie reviews and understand the purpose of the utterance, and then determine the intention behind the statement, by classifying them as either subjective or objective \n", - "31 determine the type of a sentence fragment for its tone, and identify whether it has a certain nuance, carrying a particular sentiment, or having a subjective or objective tone, to classify it as subjective or objective. \n", - "32 Determine the intent of the given text and designate it as either subjective or objective, taking into account the meaning and context \n", - "33 Classify the sentence and determine its perspective, distinguishing between subjective expressions of personal opinions or feelings and objective presentations of factual information, while considering linguistic nuances, contextual clues, and subtle linguistic cues. \n", - "34 Review the provided sentence and categorize it as either subjective (emotive) or objective (neutral), based on its linguistic and emotional characteristics. \n", - "35 Given a statement, classify it as expressing a subjective or objective opinion. \n", - "36 Analyze the question to determine its type by including categories of Description, Entity, Expression, Human, Location, and Number; categorize the question into one of the following categories: Description, Entity, Expression, Human, Location, or Number, while considering the meaning and relevant context. \n", - "37 You are given a question. You need to detect which category better describes the question. Answer with \"Description\", \"Entity\", \"Expression\", \"Human\", \"Location\", and \"Number\". \n", - "38 Your task is to choose a type of the question, from Description, Entity, Expression, Human, Location and Number. \n", - "39 Please classify each question into its primary category from the following options: Description, Entity, Expression, Human, Location, Number. \n", - "40 For each question, select the primary category (Description, Entity, Expression, Human, Location, or Number). \n", - "41 Recognize the primary category of each question, choosing from Description, Entity, Expression, Human, Location, or Number, and return the most fitting label. \n", - "\n", - " train_score test_score \n", - "0 0.95 0.885 \n", - "1 0.90 0.890 \n", - "2 1.00 0.880 \n", - "3 1.00 0.830 \n", - "4 1.00 0.880 \n", - "5 0.95 0.870 \n", - "6 0.95 0.785 \n", - "7 0.95 0.855 \n", - "8 1.00 0.855 \n", - "9 1.00 0.940 \n", - "10 1.00 0.930 \n", - "11 0.95 0.930 \n", - "12 0.95 0.885 \n", - "13 0.95 0.760 \n", - "14 0.95 0.890 \n", - "15 1.00 0.915 \n", - "16 1.00 0.905 \n", - "17 1.00 0.850 \n", - "18 0.50 0.450 \n", - "19 0.65 0.570 \n", - "20 0.55 0.400 \n", - "21 0.65 0.565 \n", - "22 0.65 0.490 \n", - "23 0.60 0.470 \n", - "24 1.00 0.940 \n", - "25 1.00 0.815 \n", - "26 1.00 0.880 \n", - "27 1.00 0.945 \n", - "28 1.00 0.935 \n", - "29 1.00 0.910 \n", - "30 0.80 0.750 \n", - "31 0.75 0.585 \n", - "32 0.85 0.630 \n", - "33 0.80 0.705 \n", - "34 0.75 0.695 \n", - "35 0.80 0.755 \n", - "36 0.55 0.630 \n", - "37 0.55 0.645 \n", - "38 0.45 0.750 \n", - "39 0.65 0.605 \n", - "40 0.55 0.685 \n", - "41 0.70 0.615 " - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ds", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/mpl_stylesheet.mplstyle b/notebooks/mpl_stylesheet.mplstyle deleted file mode 100644 index cd9ca66..0000000 --- a/notebooks/mpl_stylesheet.mplstyle +++ /dev/null @@ -1,47 +0,0 @@ -# mpl_stylesheet.mplstyle - -# Font settings -font.family: serif -font.serif: DejaVu Serif # Default serif font -font.size: 16 # General font size - -# Axes settings -axes.titlesize: 16 # Title font size -axes.labelsize: 16 # X and Y label font size -axes.labelpad: 5 # Padding between label and axis -axes.linewidth: 0.8 # Width of the axis lines - -# Tick settings -xtick.labelsize: 12 # X tick label size -ytick.labelsize: 12 # Y tick label size -xtick.major.size: 4 -ytick.major.size: 4 -xtick.minor.size: 2.5 -ytick.minor.size: 2.5 -xtick.direction: in # Ticks inside the plot -ytick.direction: in - -# Legend settings -legend.fontsize: 12 # Legend font size -legend.frameon: False # No frame around the legend - -# Grid settings -grid.linestyle: -- # Dashed grid line -grid.linewidth: 0.6 -grid.alpha: 0.7 -grid.color: gray - -# Figure settings -figure.figsize: 6.4, 4.8 # Adjust figure size as needed -figure.dpi: 300 - -# Line settings -lines.linewidth: 1.5 # Slightly thicker lines for better visibility -lines.markersize: 4 # Marker size - -# Error bars -errorbar.capsize: 2 - -# Savefig settings -savefig.dpi: 300 # High resolution for publication -savefig.bbox: tight # Trim the figure diff --git a/notebooks/optimization_progress.ipynb b/notebooks/optimization_progress.ipynb deleted file mode 100644 index 0656845..0000000 --- a/notebooks/optimization_progress.ipynb +++ /dev/null @@ -1,242 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization Progress\n", - "\n", - "In this notebook, we provide visualizations of the optimization progress." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import List\n", - "from pathlib import Path\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "plt.style.use(\"mpl_stylesheet.mplstyle\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def read_prompts(target_experiment: str, tasks: List[str]):\n", - " results = pd.DataFrame()\n", - " for logging_dir in Path(f\"../logs/{target_experiment}\").rglob(\"*.csv\"):\n", - " if \"best_scores\" in str(logging_dir): # or not any(task in str(logging_dir) for task in tasks)\n", - " continue\n", - "\n", - " result = pd.read_csv(logging_dir)\n", - "\n", - " logging_dir = str(logging_dir)\n", - "\n", - " logging_dir = logging_dir.replace(f\"..\\\\logs\\\\{target_experiment}\\\\\", \"\")\n", - " logging_dir = logging_dir.replace(\".csv\", \"\")\n", - "\n", - " task_name, optimizer, meta_llm, evaluation_llm, random_seed = logging_dir.split(\"_\")\n", - "\n", - " task_name = task_name.split(\"/\")[-1].split(\"\\\\\")[-1]\n", - "\n", - " metainformation = pd.DataFrame(\n", - " {\n", - " \"task\": [task_name] * len(result),\n", - " \"optimizer\": [optimizer] * len(result),\n", - " \"meta_llm\": [meta_llm] * len(result),\n", - " \"evaluation_llm\": [evaluation_llm] * len(result),\n", - " \"random_seed\": [random_seed] * len(result),\n", - " }\n", - " )\n", - "\n", - " result = pd.concat([result, metainformation], axis=1)\n", - "\n", - " results = pd.concat([result, results], axis=0)\n", - "\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def get_first_occurences(df):\n", - " occured = []\n", - " dss = []\n", - "\n", - " for i in range(1, df[\"step\"].max() + 1):\n", - " df_new = df.loc[(df[\"step\"] == i) & (~df[\"prompt\"].isin(occured))]\n", - " dss.append(df_new)\n", - " occured += df_new[\"prompt\"].to_list()\n", - "\n", - " return pd.concat(dss)\n", - "\n", - "\n", - "def get_plot(df1, df2=None, mean_or_max=\"mean\", task=None, optimizer=None, legend_labels=None):\n", - " df = get_first_occurences(df1).copy()\n", - " df = df[(df[\"task\"] == task) & (df[\"optimizer\"] == optimizer)]\n", - "\n", - " df_ = df.groupby(\"step\", as_index=False).agg(mean_or_max, numeric_only=True)\n", - " df_[\"score_std\"] = df.groupby(\"step\", as_index=False).std(numeric_only=True)[\"score\"]\n", - " df_.loc[df_[\"score_std\"].isna(), \"score_std\"] = 0\n", - " df = df_.copy()\n", - "\n", - " # plot average\n", - " plt.plot(\n", - " df[\"step\"],\n", - " df[\"score\"],\n", - " marker=\"+\",\n", - " markersize=8,\n", - " label=legend_labels[0] if legend_labels is not None else None,\n", - " color=sns.color_palette(\"Set2\")[0],\n", - " )\n", - " # Fill the area between std dev lines\n", - " plt.fill_between(\n", - " df[\"step\"],\n", - " df[\"score\"] + df[\"score_std\"],\n", - " df[\"score\"] - df[\"score_std\"],\n", - " alpha=0.2,\n", - " color=sns.color_palette(\"Set2\")[0],\n", - " )\n", - "\n", - " # plot second line for comparison:\n", - " if df2 is not None:\n", - " df = get_first_occurences(df2).copy()\n", - " df = df[(df[\"task\"] == task) & (df[\"optimizer\"] == optimizer)]\n", - "\n", - " df_ = df.groupby(\"step\", as_index=False).agg(mean_or_max, numeric_only=True)\n", - " df_[\"score_std\"] = df.groupby(\"step\", as_index=False).std(numeric_only=True)[\"score\"]\n", - " df_.loc[df_[\"score_std\"].isna(), \"score_std\"] = 0\n", - " df = df_.copy()\n", - "\n", - " # plot average\n", - " plt.plot(\n", - " df[\"step\"],\n", - " df[\"score\"],\n", - " marker=\"+\",\n", - " markersize=8,\n", - " label=legend_labels[1] if legend_labels is not None else None,\n", - " color=sns.color_palette(\"Set2\")[1],\n", - " )\n", - " # Fill the area between std dev lines\n", - " plt.fill_between(\n", - " df[\"step\"],\n", - " df[\"score\"] + df[\"score_std\"],\n", - " df[\"score\"] - df[\"score_std\"],\n", - " alpha=0.2,\n", - " color=sns.color_palette(\"Set2\")[1],\n", - " )\n", - "\n", - " # Customize the plot\n", - " plt.title(f\"Task {task}, Optimizer {optimizer}\")\n", - " plt.xlabel(\"Step\")\n", - " plt.ylabel(\"Score\")\n", - " plt.grid(True)\n", - " plt.legend(loc=2, bbox_to_anchor=(1, 1))\n", - "\n", - " plt.xticks(np.arange(1, 13))\n", - "\n", - " plt.ylim(0, 1.1)\n", - " plt.tight_layout()\n", - "\n", - " return plt.gca()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "df = read_prompts(r\"../logs\\experiment-task-descr\", [\":)\"])\n", - "df[\"use_task_desc\"] = True\n", - "df2 = read_prompts(r\"../logs\\experiment\", [\":)\"])\n", - "df2[\"use_task_desc\"] = False\n", - "\n", - "\n", - "df = pd.concat([df, df2])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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URCKRiHPOOScKCgoiIuKYY44p8ZvyQHYMGzYs6bhBgwax0047ZacZKrV77rmn8L9btmwZxx57bBa7IZ02bdqk1ObPn1/ieRYsWJBSa926dal6IvPSPT7Nmzcv1VzpdoeaMGFCqeaiaNkIOjz77LNJxzVq1Ih99tmnxPP07Nkz6XjixIkxe/bsMvUGZIf31gAAACCV18u5R+akcpMtgcpBToSfkwepXGQ/cod8R+UlxwGUB+8zAQBUXRY/AaDEFi9enFLr1KlTFjqhPK1bty5GjhyZVGvdunX06dMnSx1RXEOHDi3cEaRRo0Zxxx13ZLkjoLhGjRqVdLzttttmqRMqsy+++CJGjx5dePzrX/86ateuncWOSGffffdNqb377rslmmPRokXx1VdfJdXatWsX7du3L1NvZE6610YNGzYs1VyNGjVKqU2ZMqVUc1F5rFy5MsaOHZtU22677aJBgwYlnmu33XZLOk4kEjFixIgy9QdUvEQikbLrcV5eXnTs2DEr/QAAAEBlIYuSW2ROKj/ZEqgc5ET4KXmQykf2I3fId/AjOQ7IfXIZAABVmyXrACiRNWvWpLwJ36RJk9hll12y1BHlZciQIfHtt98m1QYPHhx169bNUkcUx8KFC+OKK64oPP7rX/8a7dq1y2JHQHFt2LAhJk6cmFRLF2pZv359TJ48Ob7++uuYN29e5Ofnx+abbx7t2rWLbt26WZm8GrjvvvsikUhERER+fn6cffbZWe6IdLbaaqvYd99948033yysTZkyJaZOnRqdO3cu1hyPPfZYSu2kk07KWI+UXbqg2Y87JJZUXl5eSu3LL7+MVatWZWWXGzJj6tSpsXbt2qTajjvuWKq50u3y9/7775dqLiB7vvzyy5R/Kzp37lzqneUAAAAgF8ii5B6Zk8pNtgQqBzkRfk4epPKR/cgd8h38SI4Dcp9cBgBA1ebdTgBK5N13343169cn1Y477rioUaNGljqiPLz88stx2WWXJdVOOeWUOOuss7LUEcV1ySWXxKJFiyIiolu3bnHeeedluSPK25o1a2Lq1Knx6aefxrJly2Lt2rXRvHnz2GyzzWL77be3G1oVMn369Fi+fHlSbfPNNy/8788//zxuuOGGeP7552PhwoVp52jUqFEceOCBcemll0bPnj3LtV+yY/Xq1TF06NDC40MPPTQ6dOiQxY7YmBtvvDF69eqV9Pz5iiuuKNYOH4sXL44bbrghqbb55pvHpZdemvE+Kb10H4h+9913pZpr2bJlKbUNGzbEl19+GV26dCnVnGTfxx9/nFIr7S4i6c5LNz9Quf08yB4RceKJJ2ahEwAAAKg8ZFFyi8xJ5SdbkrtkSKoWORF+Sh6k8pL9yA3yHfxIjgNyn1wGAEDVZvETAErk+eefTzrOy8uL3/72t1nqhkz77LPP4vbbb4/77rsvNmzYEBE/7CBw6aWXpnwAQ+UzZsyYeOSRRyLih8ftvvvuEwbLYa+//nrcddddMXz48Fi1alWRt9tyyy3jkEMOiYsvvrjUq9NTMQ3QCUcAAQAASURBVD799NOUWoMGDWL9+vXx5z//OW677bZN7jbx3XffxbBhw2LYsGFx+OGHx9ChQ6NFixbl1TJZ8OSTTxYGESPC87BKbt9994077rgjLrjggsLdmV588cW44IIL4o477oj8/Py05y1ZsiQGDhyYtCNivXr14vHHH4+WLVtWSO8UT9u2bVNqs2bNKtVc8+bNS1v/eeCRqmXq1KkptTZt2pRqrtatWxdrfqBy+/l7a/Xq1YszzjgjS90AAABA5SCLkhtkTqoG2ZLcJENSNcmJ8FPyIJWX7EdukO/gR3IckPvkMgAAqrb077QAQBoFBQXx8MMPJ9WOOeaY6Nq1a3YaotSWLl0at9xyS1x//fXxxz/+MU4++eTo3LlzbL/99nHvvffGhg0bIj8/Pw499NCYMGFC3HTTTUV+QEPlsGbNmjjnnHMKj88///zo1q1bFjuiPA0ZMiT233//eOqppzYaWon44QO6Bx54IDp37hzHH3980ofkVC4zZ85MqdWoUSOOOeaYuOGGG6KgoCBq164d5513XowfPz7mzZsXq1evjlmzZsUTTzwRffv2TTp3+PDhsddee8W0adMq6i5QAe69997C/95qq63ikEMOyWI3FMd5550XTz/9dNKH3XfffXd07do1/vWvf8VXX30Va9asiRUrVsSUKVPihhtuiB122CHeeOONwttvs802MWbMmDjggAOycRfYiB49eqTUpkyZUqq5Pvzww7T10u40ROUwffr0lFppA6ctWrSIvLy8pNqCBQtixYoVpZoPqHgLFiyI5557Lql23nnnpQ3FAQAAQHUhi1L1yJxUXbIluUmGpOqSE+Gn5EEqN9mPqk++gx/JcUBuk8sAAKj6ama7AQCqjkceeSTmzp1beNygQYO45ZZbstgRpbVw4cK4/PLL047VrVs3zjjjjLjsssuiU6dOFdwZpXXttdfG559/HhER7dq1i7/+9a9Z7ojy9OPOAfXq1Yvjjz8+jjnmmOjcuXO0bt06CgoKYs6cOTF27Nh4+OGHY8KECRERkUgk4sknn4yJEyfGf//739h9992zeRdIY/78+Sm122+/PVavXh0REe3bt4+XXnopOnfunHSb9u3bR/v27eO4446L++67L84999zCXUamT58e/fv3j8mTJ0ezZs3K/05Qrt57773C3+mIiLPPPltQtIo4+uijo2/fvvGvf/0rHnroofjggw9iypQp8etf/3qj5+26665x5plnxm9+85uoU6dOBXVLSfTu3Tul9uqrr0YikUgJN2zMnDlz0u7sFiEcU9Wl29mpcePGpZqrRo0aUb9+/Vi5cmXKNRo2bFiqOYGKdeedd8aaNWsKj9u2bRtXXXVVFjsCAACA7JNFqXpkTqou2ZLcJENSdcmJ8CN5kKpB9qNqk+/gR3IckNvkMgAAqj7vigFQLCtWrEh50X/DDTdEhw4dstQR5WX16tVxzz33xC677BKnnnpq0srzVE7Tpk2LG2+8sfD4jjvuiEaNGmWxIypC7969Y8qUKTF06NA4/PDDo1OnTlGvXr1o0qRJ7LDDDnHuuefGW2+9Ff/617+idu3ahefNnDkz+vbtWxhoovJYsmRJSu3HQEu9evVi1KhRKYGWnzv77LOT/h5ERMyYMWOTH7JTNfx0l586derEGWeckcVuKKnGjRvHUUcdFaeddlr069dvo8GJ2rVrx2mnnRZXX311nHHGGcIvldjOO+8c3bt3T6p9++23MW7cuBLN85///Cc2bNiQdmxTO/RRuaXbzadBgwalni/duXYMgqrh66+/jltvvTWpNmTIkFIH6QAAACAXyKLkHpmTyku2JLfJkFRNciL8SB6k6pD9qLrkO/iRHAfkLrkMAIDcYPETAIrl0ksvjTlz5hQeDxw4MM4///wsdkRZbLPNNpFIJCKRSMSaNWtizpw58dZbb8VNN90UXbp0iYgf3nh9+OGHY7/99otjjz026fGncjnnnHOioKAgIiIOO+ywOOaYY7LcEeXt0EMPjZdffjm23nrrTd729NNPj6eeeirpg9YlS5bEgAEDUlabJ7t+DLCkc8UVV8QOO+xQrHkuueSSwr/lP3r22Wfj7bffLlN/ZNfy5cvjP//5T+HxL37xi2jRokUWO6Ikpk6dGv37949OnTrFRRddFC+99FJEROy2225x3nnnxTXXXBODBg2Ks846K3bYYYcoKCiIBx98MI466qho3bp1XHzxxbF06dLs3gmK9Pvf/z6l9qc//anY5y9dujQlkPhTPw2gUvWk29mpZs2apZ4v3bl2j4LKL5FIxJlnnpkUeLzwwgvjiCOOyGJXAAAAkH2yKFWTzEnVJFuSu2RIqi45ESLkQaoS2Y+qT76DCDkOyFVyGQAAucPiJwBs0rBhw+L+++8vPN5+++3jwQcfzF5DZFTt2rWjdevWsffee8fll18eH374Yfz73/9OWon6mWeeib333js++eSTLHZKOkOHDo3x48dHRET9+vXj7rvvznJHlJfWrVvH6NGjY/To0fHMM8+U6IOyAQMGpOzoMm3atLjzzjsz3SZlsHbt2rT1mjVrxnnnnVfseWrUqBGXXHJJSv2mm24qdW9k30MPPZQUNvvtb3+bxW4oiXvuuSe6du0aI0eOjEQiERERBx10UEyZMiUmT54cd999d1x11VVx9dVXx3333ReffPJJvPnmm9GtW7eI+CHodPvtt8d2221nd8RK6phjjokBAwYk1d5666248sorN3nuunXr4pRTTokFCxYUeRu7P1Vt6XZ2qlGjRqnnSxeasXsUVH533HFHvPzyy4XHPXv29PwcAACAak8WJTfInFQNsiW5R4YkN8iJECEPUlXIfuQG+Q4i5DggV8llAADkDoufALBRH330UZxyyimFx5tttlkMHz48mjZtmr2mKFd5eXlx0kknxfjx46Nu3bqF9VmzZkW/fv1i8eLFWeyOn1q4cGFcfvnlhceDBg2Kjh07Zq8hylXdunWjb9++0bdv36TfzeIaNGhQ5OcnP/2/9dZbY8WKFZlqkTKqV69e2vr+++8fm2++eYnmOvLII1M+VBsxYkThTl5UPUOGDCn8765du8Y+++yTxW4orjvuuCPOO++8WLduXWHt17/+dYwcOTI6d+5c5Hk9evSI119/Pfr3719YW7BgQRx00EExduzYcu2ZksvLy4uHHnoott1226T69ddfH2eccUaRz5+/+OKLOOSQQ+KFF16IiIgtt9wyttpqq5TbNW/ePPNNU2HS/fu+fv36Us+X7tyinkMAlcOrr76a9Np96623jmHDhtn5DQAAgGpNFiV3yZxUPrIluUmGJDfIiRAhD1IVyH7kDvkOIuQ4IBfJZQAA5BaLnwBQpDlz5sSAAQPiu+++i4iIRo0axYsvvhjbbLNNljujInTr1i1uueWWpNqsWbNKtKsE5evSSy+NRYsWRUTEzjvvnHYHD/jRFltsEQcccEBSbdGiRTFq1KgsdcTPNWrUKG29e/fuJZ6rWbNmsd122yXVVq9eHW+//XapeiO7xo8fHx9//HHh8bnnnpvFbiiud955Jy677LKkWteuXeOee+5JCRKmU69evXj00UejXbt2hbXVq1fHiSeeGPPmzct4v5RN06ZN43//+1/07NkzqT506NBo27ZtHHHEEfH73/8+rrvuurjkkkti//33j+233z7GjBkTEVG4u1O6YH/Lli0r4i5QTtL9+/7TUFxJpTu3qOcQQPZ9/PHH8Ytf/KLwd7dNmzbx8ssvx2abbZblzgAAACB7ZFGqB5mTykO2hHRkSCoHORHkQSo/2Y/cI9+BHAfkFrkMAIDcY/ETANJavHhxHHTQQfHVV19FxA9vwD///POx5557ZrkzKtKZZ54Zbdu2Tao9+eSTMW3atCx1xI/Gjh0bDz/8cET8sBr9kCFDUnbvgJ/bb7/9UmqvvvpqFjohnaI+8Npxxx1LNV+6nUUmTZpUqrnIrnvuuafwv5s0aRInnXRSFruhuP70pz+lfLg9ePDgEu0m0KxZs6QdCSIi5s2bFzfeeGNGeiSzWrRoEa+++mrccsstSTuxrVmzJoYPHx433XRT/OlPf4q//e1v8frrr8eGDRuiTp06ceWVV8bkyZOjffv28f333yfNmZeXF1tuuWVF3xUyqGHDhim1lStXlnq+dDsuprsGkH1fffVVHHTQQbFkyZKI+GEH69GjR8fWW2+d5c4AAAAge2RRqheZk+yTLWFjZEiyT04EeZDKT/YjN8l3VG9yHJA75DIAAHKTxU8ASLF06dI45JBDYurUqRHxQ9jkueeei969e2e3MSpc7dq149hjj02qbdiwIR599NEsdUTEDx+wnHPOOYXHZ555ZvTo0SOLHVFV7Lbbbim1iRMnZqET0mnSpEnaerNmzUo1X7qdJBYuXFiqucieefPmxbPPPlt4fMopp0SDBg2y2BHFMWPGjBg9enRSbbPNNovDDz+8xHOdcsopkZeXl1R74IEHYu3atWXqkfJRu3btuPTSS+Orr76KJ554In7961/HLrvsEq1bt47atWtH06ZNY/vtt48jjjgihgwZEjNmzIhrr7228Pf652GKLbbYIurXr5+Nu0KGNG7cOKX24462JbVhw4aUAFWEHYOgMvr666/jwAMPjNmzZ0fED88DXn311bTBcwAAAKguZFGqH5mT7JItYVNkSLJPTqR6kwep/GQ/cpt8R/UlxwG5QS4DACB3WcIdgCTLli2Lgw8+ON59992IiKhbt24MGzYsDjrooCx3Rrb06NEj7rzzzqTauHHjstMMEfHDh16fffZZ4XGLFi3illtuKfb5P4bJfuqJJ54o/L3/ueOPPz7at29f8kapdFq1apVSmz9/fhY6IZ1tttkmbb20uwCk+wBt8eLFpZqL7PnHP/6RFHQ499xzs9gNxTVmzJiU2t577x35+SVfg7ZZs2ax4447xscff1xYW7FiRUyYMCHtbmxUDg0aNIjjjjsujjvuuGKfs379+pg7d25SrWvXrhnujIrWqVOnlFppQ6YLFy6MRCKRVGvRooXQDFQy33zzTfTp06dwB+vmzZvHK6+8ErvuumuWOwMAAIDskUWpvmROske2hE2RIck+OZHqTR6k8pP9qB7kO6ofOQ6o+uQyAABym8VPACi0fPnyOOSQQ+Kdd96JiIg6derEsGHD4uCDD85yZ2RThw4dUmrTp0/PQif86Odvsl933XVlnnPIkCFFjnXv3l1AJUek2zHGDi+Vx84775y2XlBQUKr5fr5bSESkfNBG5bZ+/fq4//77C4979+4dO+64YxY7orh+GiT9UceOHUs9X8eOHZMCMBER06ZNE4DJMTNmzEjZ1WmfffbJUjdkSrrdRH7ccaSk5syZU6z5gez59ttvo0+fPvHll19GxA9B1ldeeUXYEQAAgGpNFqV6kznJHtkSNkWGJPvkRKoveZCqQfaDosh3VG1yHFC1yWUAAOS+ki87C0BO+jFsMnHixIj4IWzy7LPPxiGHHJLlzsi25s2bp9QWLVqUhU6AskoXaEgXfCA72rVrF82aNUupr1ixolTzfffddym1zTffvFRzkR0jRoyIWbNmFR7/9re/zWI3lES63bOaNm1a6vnSnSt4mHu++OKLlJrXY1XfTjvtlFKbMWNGqeZKd166+YHs+Pbbb6N3796Ff8+bNm0ao0ePjt122y3LnQEAAED2yKIgcwKVlwxJ9smJVF/yIFWD7AdFke+o2uQ4oOqSywAAqB4sfgJAfPfdd9GvX7+YMGFCRETUrl07nn766ejfv3+WO6O0VqxYEe+//368//77SR+SlcaaNWtSanXr1i3TnJTN1VdfHYlEotRfp556asqcY8eOLfL2vXv3rvg7SblYtmxZSq1FixZZ6ISipNsBorS7CixYsCCl1rJly1LNRXbce++9hf/dpk2bOOqoo7LYDSVRv379lNrq1atLPV+6c+vVq1fq+aicXn/99aTjLbfcMnbfffcsdUOmdO7cOWrVqpVU++STT0o11893AYsIu5ZAJTF79uzo06dPYcCmSZMmMWrUqOjWrVuWOwMAAIDskUWpumROcoNsCZsiQ1I5yIlUT/IgVYPsB0WR76ja5DigapLLAACoPmpmuwEAsuvHsMlbb70VERG1atWKp556Kg4//PBinX/ZZZdFxA87tVx55ZXl1icl8+6770afPn0iIuLoo4+OZ555ptRzzZ8/P6XWqlWrUs8HlNyUKVMKQyd77LFH1KlTp1TzzJs3L6Vmh5fK5dhjj40XX3wxqTZ16tRSzZXuvL333rtUc1Hxpk+fHi+//HLh8W9+85uoWdNL+Koi3d/WsuximG6nHyG13DNy5Mik49NPPz1LnZBJDRs2jN69e8fo0aMLa59//nl8//33acNyG/Pee+8lHefl5RX7tTtQfubMmRN9+vSJzz//PCIiGjduHC+//HLssccemzx3yZIlce2110ZExNZbbx3nnntuufYKAAAAFUUWpWqTOYHKTYYkt8iJVD/yIFWH7AdFke+o2uQ4oOqRywAAqF7ys90AANmzYsWK6N+/f/zvf/+LiB/CJk8++WQMGDCg2HPceuutceutt8b9999fXm1SRt9++22Zzp84cWJKbbvttivTnEDJXHDBBbHffvvFfvvtF5999lmp55k8eXJKrThv/FJxjjrqqKhdu3ZS7cd/p0ti+fLl8emnnybVmjdvHrvttluZ+qPiDBkyJBKJRERE1KhRI84+++wsd0RJ7LDDDim1dH+DiyORSMQHH3yQUt92221LNR/lY968eTF37txYsmRJqc6fMWNG0s9I3bp146yzzspUe2TZz3dqW7duXeH/9FESb7zxRtLxnnvuGW3bti1Tb0DZzJ07N/r06VP4Oq1Ro0bx8ssvx1577VWs85ctW1b43toTTzxRnq0CAABAhZFFyS0yJ1D5yJDkFjmR6kcepOqQ/chN8h1EyHFAVSKXAQBQ/Vj8BKCaWrlyZRx66KHx5ptvRkREzZo14/HHH48jjzwyu42RcZMnT47vvvuu1Oc/++yzKbXDDjusLC0BZVCWcNnrr7+eUuvbt29Z2iHDmjZtGscee2xSberUqSkBlU157rnnYu3atUm1E044IfLzvQSsCtasWRNDhw4tPB4wYEC0a9cuix1RUn379k3Zmemjjz4q1d/wt956q3Dnth+1aNEiunXrVqYeyax27dpFmzZtiv2h6s9dd911hQG3iIjzzjtPGCKHHHnkkVGjRo2k2s938NuUyZMnx5w5c5JqP3/OAFSsefPmxQEHHFD4XL1Ro0YxcuRIu2gCAABQrcmi5B6ZE6jcZEiqPjmR6kUepGqR/chN8h1EyHFAVSGXAQBQPXlHE6Aa+jFs8uMHmDVr1ozHHnssjj766Cx3RnlYu3ZtqXdDeumll+Ldd99NqtWvX18wCbLotddeK9V5M2fOjLFjxybVmjZtGgcddFAm2iKDrrvuuqhbt25S7aabbir2+YlEIu68886kWp06deKPf/xjRvqj/D355JOxcOHCwuNzzz03i91QGk2aNIn+/fsn1TZs2BB33XVXiee6/fbbU2rHHXeckFol9fnnn8cXX3xRonOmTp0aDz30UOFx+/bt4//+7/8y3RpZ1KZNmzjllFOSao899lisW7eu2HM88sgjScfNmzePM888MyP9ASU3b9686NOnT3zyyScREdGwYcN48cUXo0ePHlnuDAAAALJHFiU3yZxA5SZDkhvkRKoPeZCqRfYjt8l3VG9yHFD5yWUAAFRf3i0BqGa+//77OOywwwo/+KxRo0Y8+uijVhrOcX/5y1/i448/LtE533zzTZx++ukp9T/84Q/RunXrTLUGlNBDDz0Uq1evLvF5V199ddKOAxERF198cTRu3DhTrZEhHTp0iIsvvjip9tBDD8Ubb7xRrPOHDBmSEiK88sorY4sttshYj5Sve++9t/C/t9tuO7trVVHXXXddSkjltttui3feeafYcwwbNiyeeuqppFrDhg3jqquuykiPlI+//vWvxb7tokWLYsCAAVFQUBAREbVq1YpHHnnEv8856Oqrr446deoUHs+ZMyclCFOURYsWxT//+c+k2u9///to0qRJRnsEimf+/PlxwAEHFAZsGjRoECNGjIiePXtmuTMAAADIHlmU3CZzApWXDElukBOpPuRBqh7Zj9wm31G9yXFA5SWXAQBQvVn8BKAa+TFsMn78+MLa+vXr4/jjj4+8vLxSfVE1LFu2LPr27RujRo0q1u0nTpwY++yzT8ybNy+pvv/++8fll19eHi0CxTR79uwS78zy3//+Nx588MGkWqdOneKiiy7KXGNk1KBBg2KvvfYqPF6/fn0MHDgw3n777Y2e9/jjj8fvfve7pNqAAQN8WF6FfPDBB/HWW28VHp9zzjmec1VRXbp0iWuuuSaptnbt2ujXr1/hrpcb8/TTT8eJJ56YVMvLy4u7775bKLiSe+ihh+K2227b5O3eeeed6NG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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 25))\n", - "\n", - "for i, task in enumerate([\"agnews\", \"cr\", \"mr\", \"sst2\", \"sst-5\", \"subj\", \"trec\"]):\n", - " for j, optimizer in enumerate([\"evopromptde\", \"evopromptga\"]):\n", - " plt.subplot(7, 2, 2 * i + j + 1)\n", - "\n", - " get_plot(\n", - " df1=df[~df[\"use_task_desc\"]],\n", - " df2=df[df[\"use_task_desc\"]],\n", - " mean_or_max=\"mean\",\n", - " task=task,\n", - " optimizer=optimizer,\n", - " legend_labels=(\"no task descr.\", \"task descr.\"),\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ds", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/test_train_score_correlation.ipynb b/notebooks/test_train_score_correlation.ipynb deleted file mode 100644 index 0b8e1c5..0000000 --- a/notebooks/test_train_score_correlation.ipynb +++ /dev/null @@ -1,457 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from pathlib import Path\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from typing import List" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "def read_prompts(target_experiment: str, tasks: List[str]):\n", - " results = pd.DataFrame()\n", - " for logging_dir in Path(f\"../logs/{target_experiment}\").rglob(\"*.csv\"):\n", - " if \"best_scores\" in str(logging_dir):\n", - " continue\n", - "\n", - " result = pd.read_csv(logging_dir)\n", - "\n", - " logging_dir = str(logging_dir)\n", - "\n", - " logging_dir = logging_dir.replace(f\"..\\\\logs\\\\{target_experiment}\\\\\", \"\")\n", - " logging_dir = logging_dir.replace(\".csv\", \"\")\n", - "\n", - " task_name, optimizer, meta_llm, evaluation_llm, random_seed = logging_dir.split(\"_\")\n", - "\n", - " metainformation = pd.DataFrame(\n", - " {\n", - " \"task\": [task_name] * len(result),\n", - " \"optimizer\": [optimizer] * len(result),\n", - " \"meta_llm\": [meta_llm] * len(result),\n", - " \"evaluation_llm\": [evaluation_llm] * len(result),\n", - " \"random_seed\": [random_seed] * len(result),\n", - " }\n", - " )\n", - "\n", - " result = pd.concat([result, metainformation], axis=1)\n", - "\n", - " results = pd.concat([result, results], axis=0)\n", - "\n", - " return results\n", - "\n", - "\n", - "def read_best_scores(target_experiment: str):\n", - " return pd.read_csv(f\"../logs/{target_experiment}/best_scores.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "df_train = read_prompts(f\"experiment\", \"\")\n", - "df_test = read_best_scores(f\"experiment\")\n", - "\n", - "df_train.random_seed = df_train.random_seed.astype(int)\n", - "df_test.random_seed = df_test.random_seed.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "df_train[\"evaluation_llm\"] = df_train[\"evaluation_llm\"].str.replace(\"\\\\\", \"/\", regex=False)\n", - "df_train[\"meta_llm\"] = df_train[\"meta_llm\"].str.replace(\"\\\\\", \"/\", regex=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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scoretest_score
score1.0000000.891781
test_score0.8917811.000000
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" - ], - "text/plain": [ - " score test_score\n", - "score 1.000000 0.891781\n", - "test_score 0.891781 1.000000" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluation_llm = meta_llm = \"meta-llama/Meta-Llama-3-70B-Instruct\"\n", - "\n", - "group_cols = [\"task\", \"optimizer\", \"meta_llm\", \"evaluation_llm\", \"random_seed\"]\n", - "\n", - "df1 = df_train[(df_train[\"meta_llm\"] == meta_llm) & (df_train[\"evaluation_llm\"] == evaluation_llm)]\n", - "\n", - "# find prompt which got the best score first, per seed\n", - "df1 = df1.sort_values(\"score\", ascending=False) #\n", - "df1 = df1.groupby(group_cols).first()\n", - "\n", - "df2 = df_test[\n", - " # (df_test[\"task\"] == task)\n", - " (df_test[\"meta_llm\"] == meta_llm)\n", - " & (df_test[\"evaluation_llm\"] == evaluation_llm)\n", - "]\n", - "\n", - "df2 = df2.set_index(group_cols)\n", - "\n", - "df1 = df1[[\"score\"]]\n", - "df1.loc[:, \"test_score\"] = df2.loc[df1.index, [\"test_score\"]]\n", - "\n", - "sns.scatterplot(data=df1, x=\"score\", y=\"test_score\", hue=\"task\")\n", - "# trendline black with dashed\n", - "sns.regplot(data=df1, x=\"score\", y=\"test_score\", scatter=False, color=\"black\", line_kws={\"linestyle\": \"dashed\"})\n", - "plt.show()\n", - "\n", - "df1.corr()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " score test_score\n", - "score 1.000000 0.769682\n", - "test_score 0.769682 1.000000" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluation_llm = meta_llm = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n", - "\n", - "group_cols = [\"task\", \"optimizer\", \"meta_llm\", \"evaluation_llm\", \"random_seed\"]\n", - "\n", - "df1 = df_train[(df_train[\"meta_llm\"] == meta_llm) & (df_train[\"evaluation_llm\"] == evaluation_llm)]\n", - "\n", - "# find prompt which got the best score first, per seed\n", - "df1 = df1.sort_values(\"score\", ascending=False) #\n", - "df1 = df1.groupby(group_cols).first()\n", - "\n", - "df2 = df_test[\n", - " # (df_test[\"task\"] == task)\n", - " (df_test[\"meta_llm\"] == meta_llm)\n", - " & (df_test[\"evaluation_llm\"] == evaluation_llm)\n", - "]\n", - "\n", - "df2 = df2.set_index(group_cols)\n", - "\n", - "df1 = df1[[\"score\"]]\n", - "df1.loc[:, \"test_score\"] = df2.loc[df1.index, [\"test_score\"]]\n", - "\n", - "sns.scatterplot(data=df1, x=\"score\", y=\"test_score\", hue=\"task\")\n", - "# trendline black with dashed\n", - "sns.regplot(data=df1, x=\"score\", y=\"test_score\", scatter=False, color=\"black\", line_kws={\"linestyle\": \"dashed\"})\n", - "plt.show()\n", - "\n", - "df1.corr()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "group_cols = [\"task\", \"optimizer\", \"meta_llm\", \"evaluation_llm\", \"random_seed\"]\n", - "\n", - "df1 = df_train\n", - "\n", - "# find prompt which got the best score first, per seed\n", - "df1 = df1.sort_values(\"score\", ascending=False) #\n", - "df1 = df1.groupby(group_cols).first()\n", - "\n", - "df2 = df_test\n", - "\n", - "df2 = df2.set_index(group_cols)\n", - "\n", - "df1 = df1[[\"score\"]]\n", - "df1.loc[:, \"test_score\"] = df2.loc[df1.index, [\"test_score\"]]\n", - "df1 = df1.reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "df1.loc[df1[\"meta_llm\"] == \"meta-llama/Meta-Llama-3-70B-Instruct\", \"meta_llm\"] = \"Llama-70B\"\n", - "df1.loc[df1[\"evaluation_llm\"] == \"meta-llama/Meta-Llama-3-70B-Instruct\", \"evaluation_llm\"] = \"Llama-70B\"\n", - "df1.loc[df1[\"meta_llm\"] == \"meta-llama/Meta-Llama-3-8B-Instruct\", \"meta_llm\"] = \"Llama-8B\"\n", - "df1.loc[df1[\"evaluation_llm\"] == \"meta-llama/Meta-Llama-3-8B-Instruct\", \"evaluation_llm\"] = \"Llama-8B\"\n", - "df1.loc[df1[\"optimizer\"] == \"evopromptde\", \"optimizer\"] = \"DE\"\n", - "df1.loc[df1[\"optimizer\"] == \"evopromptga\", \"optimizer\"] = \"GA\"" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "df1[\"score\"] *= 100\n", - "df1[\"test_score\"] *= 100" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "df1[\"diff\"] = df1[\"test_score\"] - df1[\"score\"]\n", - "df1 = df1.rename(columns={\"score\": \"train_score\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "diff_df = (\n", - " df1.groupby([\"task\", \"optimizer\", \"meta_llm\", \"evaluation_llm\"])\n", - " .mean()\n", - " .reset_index()\n", - " .drop([\"random_seed\", \"evaluation_llm\"], axis=1)\n", - " .round(2)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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train_scoretest_scorediff
meta_llmoptimizer
Llama-70BDE89.7679.74-10.02
GA92.1478.26-13.88
Llama-8BDE81.6774.52-7.14
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" - ], - "text/plain": [ - " train_score test_score diff\n", - "meta_llm optimizer \n", - "Llama-70B DE 89.76 79.74 -10.02\n", - " GA 92.14 78.26 -13.88\n", - "Llama-8B DE 81.67 74.52 -7.14\n", - " GA 85.00 74.50 -10.50" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff_df.groupby([\"meta_llm\", \"optimizer\"]).mean(numeric_only=True).round(2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ds", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/poetry.lock b/poetry.lock index 1938d9c..1d7b871 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,122 +2,109 @@ [[package]] name = "aiohappyeyeballs" 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ae4ed93..7e386a2 100644 --- a/promptolution/optimizers/__init__.py +++ b/promptolution/optimizers/__init__.py @@ -39,9 +39,6 @@ def get_optimizer( Raises: ValueError: If an unknown optimizer type is specified in the config. """ - if config.optimizer == "dummy": - return DummyOptimizer(*args, **kwargs) - if optimizer is None: optimizer = config.optimizer @@ -51,22 +48,12 @@ def get_optimizer( if config is not None and meta_prompt is None: meta_prompt = config.meta_prompt + if optimizer == "dummy": + return DummyOptimizer(*args, **kwargs) if config.optimizer == "evopromptde": - prompt_template = EVOPROMPT_DE_TEMPLATE_TD if include_task_desc else EVOPROMPT_DE_TEMPLATE - prompt_template = meta_prompt if meta_prompt else prompt_template - donor_random = kwargs.get("donor_random", config.donor_random if config is not None else None) - return EvoPromptDE(donor_random=donor_random, prompt_template=prompt_template, *args, **kwargs) - + return EvoPromptDE(donor_random=config.donor_random, *args, **kwargs) if config.optimizer == "evopromptga": - prompt_template = EVOPROMPT_GA_TEMPLATE_TD if config.include_task_desc else EVOPROMPT_GA_TEMPLATE - prompt_template = config.meta_prompt if meta_prompt else prompt_template - selection_mode = kwargs.get("selection_mode", config.selection_mode if config is not None else None) - return EvoPromptGA(selection_mode=selection_mode, prompt_template=prompt_template, *args, **kwargs) - + return EvoPromptGA(selection_mode=config.selection_mode, *args, **kwargs) if config.optimizer == "opro": - prompt_template = OPRO_TEMPLATE - prompt_template = config.meta_prompt if config.meta_prompt else prompt_template - n_samples = kwargs.get("n_samples", config.n_ds_samples_to_meta if config is not None else None) - return Opro(prompt_template=prompt_template, n_samples=n_samples, *args, **kwargs) - + return Opro(*args, **kwargs) raise ValueError(f"Unknown optimizer: {config.optimizer}") diff --git a/promptolution/optimizers/opro.py b/promptolution/optimizers/opro.py index b2fa645..3c71f4e 100644 --- a/promptolution/optimizers/opro.py +++ b/promptolution/optimizers/opro.py @@ -39,7 +39,8 @@ def __init__(self, meta_llm: BaseLLM, n_samples: int = 2, prompt_template: str = self.meta_prompt = self.meta_prompt.replace("", self.task.description) self.scores = [ - self.task.evaluate(p, self.predictor, subsample=True, n_samples=self.n_eval_samples) for p in self.prompts + self.task.evaluate(p, self.predictor, subsample=True, n_samples=self.n_eval_samples)[0] + for p in self.prompts ] def _sample_examples(self): @@ -61,7 +62,10 @@ def _format_old_instructions(self): str: The formatted string of previous prompts and their scores. """ return "".join( - [f"Old instruction: {prompt}\nScore: {score}\n\n" for prompt, score in zip(self.prompts, self.scores)] + [ + f"The old instruction was:\n{prompt}\nIt scored: {score}\n\n" + for prompt, score in zip(self.prompts, self.scores) + ] ) def optimize(self, n_steps: int) -> List[str]: diff --git a/promptolution/tasks/__init__.py b/promptolution/tasks/__init__.py index 44d7f69..9c0f7b4 100644 --- a/promptolution/tasks/__init__.py +++ b/promptolution/tasks/__init__.py @@ -39,16 +39,15 @@ def get_task( - For all other tasks, a ClassificationTask instance is created. - The task description is loaded from a 'description.json' file in the dataset path. """ - if config.task_name == "dummy": - task = DummyTask() - return task - if ds_path is None: ds_path = config.ds_path if task_name is None: task_name = config.task_name if random_seed is None: random_seed = config.random_seed + if task_name == "dummy": + task = DummyTask() + return task task_description_path = Path(ds_path) task = ClassificationTask(task_description_path, task_name, split=split, seed=random_seed) diff --git a/promptolution/tasks/classification_tasks.py b/promptolution/tasks/classification_tasks.py index f37deec..78f5c99 100644 --- a/promptolution/tasks/classification_tasks.py +++ b/promptolution/tasks/classification_tasks.py @@ -5,6 +5,7 @@ from typing import Callable, Dict, List, Literal, Optional import numpy as np +import pandas as pd from sklearn.metrics import accuracy_score from promptolution.predictors.base_predictor import BasePredictor @@ -20,6 +21,7 @@ class ClassificationTask(BaseTask): Attributes: task_id (str): Unique identifier for the task. path (Path): Path to the dataset description JSON file, and initial prompts. + path (Path): Path to the dataset description JSON file, and initial prompts. dataset_json (Dict): Dictionary containing dataset information. description (Optional[str]): Description of the task. initial_population (Optional[List[str]]): Initial set of prompts. @@ -64,6 +66,62 @@ def __init__( self._parse_task() self.reset_seed(seed) + @classmethod + def from_dataframe( + cls, + df: pd.DataFrame, + description: str, + x_column: str = "x", + y_column: str = "y", + task_id: str = "Classification Task", + initial_prompts: List[str] = None, + seed: int = 42, + split: Literal["dev", "test"] = "dev", + metric: Callable = accuracy_score, + ) -> "ClassificationTask": + """Create a ClassificationTask instance from a pandas DataFrame. + + Args: + df (pd.DataFrame): Input DataFrame containing the data + x_column (str): Name of the column containing input texts + y_column (str): Name of the column containing labels + task_id (str): Unique identifier for the task + description (str, optional): Description of the task + initial_prompts (List[str], optional): Initial set of prompts + seed (int): Random seed for reproducibility + split (Literal["dev", "test"]): Dataset split to use + metric (Callable): Metric to use for evaluation + + Returns: + ClassificationTask: A new instance initialized with the DataFrame data + """ + instance = cls.__new__(cls) + + # Initialize basic attributes + instance.task_id = task_id + instance.path = None + instance.dataset_json = None + instance.description = description + instance.initial_population = initial_prompts or [] + instance.metric = metric + instance.split = split + + # Sort classes by frequency + value_counts = df[y_column].value_counts() + instance.classes = value_counts.index.tolist() + + # Create a mapping from class labels to integers + label_to_int = {label: i for i, label in enumerate(instance.classes)} + + # Set data attributes + instance.xs = df[x_column].values + instance.ys = df[y_column].map(label_to_int).values # Convert labels to integers + + # Set seed + instance.reset_seed(seed) + + return instance + def __str__(self): """Convert task to string representation, returning the task id.""" return self.task_id @@ -116,6 +174,9 @@ def evaluate( subsample (bool, optional): Whether to use subsampling. If set to true, samples a different subset per call. Defaults to False. return_seq (bool, optional): whether to return the generating sequence + subsample (bool, optional): Whether to use subsampling. + If set to true, samples a different subset per call. Defaults to False. + return_seq (bool, optional): whether to return the generating sequence Returns: np.ndarray: Array of accuracy scores for each prompt. diff --git a/pyproject.toml b/pyproject.toml index 359f9a9..dcb00ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,12 @@ [tool.poetry] name = "promptolution" -version = "1.0.1" +version = "1.1.0" description = "" authors = ["Tom Zehle, Moritz Schlager, Timo Heiß"] readme = "README.md" [tool.poetry.dependencies] -python = "^3.11" +python = "^3.9" numpy = "^1.26.0" langchain-anthropic = "^0.1.22" langchain-openai = "^0.1.21" diff --git a/scripts/experiment_evaluation.py b/scripts/experiment_evaluation.py deleted file mode 100644 index 027b2dd..0000000 --- a/scripts/experiment_evaluation.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Experiment for paper Towards Cost-Effective Prompt Tuning to evaluate best prompts.""" -from argparse import ArgumentParser -from configparser import ConfigParser -from logging import INFO, Logger -from pathlib import Path -from typing import Tuple - -import pandas as pd -from tqdm import tqdm - -from promptolution.config import Config -from promptolution.predictors import get_predictor -from promptolution.tasks import get_task - -logger = Logger(__name__) -logger.setLevel(INFO) - - -def get_best_prompt_from_csv(csv_path: str) -> Tuple[str, float]: - """Get the best prompt from a csv file. - - This function gets the best prompst from a csv file by looking at the most recent step - and picking the prompt with the highest score in that step. There are multiple occurrences of the same step, - we only pick the highest score (first occurrrence if there are multiple). - """ - df = pd.read_csv(csv_path) - max_step = df["step"].max() - df = df[df["step"] == max_step] - best_score = df["score"].max() - best_prompt = df[df["score"] == best_score]["prompt"].values[0] - return best_prompt, best_score - - -def evaluate_best_prompts( - experiment_name: str, target_experiment: str, logging_dir: Path, downstream_llm: str, n_samples: int, seed: int -) -> pd.DataFrame: - """Evaluate the best prompts from a csv file.""" - # convert the logging directory to a string - logging_dir = str(logging_dir) - - best_prompt, best_score = get_best_prompt_from_csv(logging_dir) - # extract information about the experiment from the filename - logging_dir = logging_dir.replace(f"logs\\{target_experiment}\\", "") - logging_dir = logging_dir.replace(".csv", "") - - task_name, optimizer, meta_llm, evaluation_llm, random_seed = logging_dir.split("_") - - # create config for the experiment - config = Config( - task_name=task_name, - ds_path=f"data_sets/cls/{task_name}", - random_seed=seed, - ) - - # create a test task to retrieve the samples to evaluate - test_task = get_task(config, split="test") - test_predictor = get_predictor(downstream_llm, classes=test_task.classes) - - # evaluate the best prompt on the test set - test_score = test_task.evaluate(best_prompt, test_predictor, subsample=True, n_samples=n_samples) - - # save the test score to a csv file - df = pd.DataFrame( - { - "task": task_name, - "optimizer": optimizer, - "meta_llm": meta_llm, - "downstream_llm": downstream_llm, - "evaluation_llm": evaluation_llm, - "random_seed": random_seed, - "best_prompt": best_prompt, - "dev_score": best_score, - "test_score": test_score, - }, - index=[0], - ) - df.to_csv(f"logs/{experiment_name}/best_scores.csv", mode="a", header=False, index=False) - - -def main(): - """Run the evaluation of best prompts.""" - # read experiments - arg_parser = ArgumentParser() - arg_parser.add_argument("-e", "--experiment", type=str, help="Experiment Config Filepath") - args = arg_parser.parse_args() - all_configs = ConfigParser() - all_configs.read(args.experiment) - - experiment_name = all_configs["experiment"]["name"] - target_experiment = all_configs["target_experiment"]["name"] - downstream_llms = all_configs["downstream_llm"]["name"].split(",") - tasks = all_configs["task"]["task_name"].split(",") - optimizers = all_configs["task"]["optimizer"].split(",") - llms = all_configs["task"]["llm"].split(",") - seed = int(all_configs["task"]["subsample_seed"]) - n_samples = int(all_configs["task"]["n_samples"]) - - logger.critical(f"Starting evaluation of best prompts for experiment {target_experiment}") - - for downstream_llm in downstream_llms: - logger.critical(f"Downstream LLM: {downstream_llm}") - # iterate through all files in the target experiment folder - for logging_dir in tqdm(Path(f"logs/{target_experiment}").rglob("*.csv")): - if ( - "best_scores" in str(logging_dir) - or not any(task in str(logging_dir) for task in tasks) - or not any(optimizer in str(logging_dir) for optimizer in optimizers) - or not any(llm in str(logging_dir) for llm in llms) - ): - continue - - print(logging_dir) - # extract the logging directory from the file path by removing the directory and experiment name - evaluate_best_prompts( - experiment_name=experiment_name, - target_experiment=target_experiment, - logging_dir=logging_dir, - downstream_llm=downstream_llm, - n_samples=n_samples, - seed=seed, - ) - - logger.critical("Evaluation of best prompts finished.") - - -if __name__ == "__main__": - main() diff --git a/scripts/experiment_initial_prompts.py b/scripts/experiment_initial_prompts.py deleted file mode 100644 index b05ed0e..0000000 --- a/scripts/experiment_initial_prompts.py +++ /dev/null @@ -1,121 +0,0 @@ -"""Experiments for paper Towards Cost-Effective Prompt Tuning initial prompt evaluation.""" -from argparse import ArgumentParser -from configparser import ConfigParser -from logging import INFO, Logger -from pathlib import Path -from typing import List - -import numpy as np -import pandas as pd - -from promptolution.config import Config -from promptolution.predictors import get_predictor -from promptolution.tasks import get_task - -logger = Logger(__name__) -logger.setLevel(INFO) - - -def evaluate_prompts( - experiment_name: str, - downstream_llm: str, - task_name: str, - random_seed: int, - n_samples: int, - prompts: List[str], -) -> pd.DataFrame: - """Evaluate the best prompts from a csv file.""" - # create config for the experiment - config = Config( - task_name=task_name, - ds_path=f"data_sets/{task_name}", - random_seed=random_seed, - ) - - # create a test task to retrieve the samples to evaluate - test_task = get_task(config, split="test") - test_predictor = get_predictor(downstream_llm, classes=test_task.classes) - - # evaluate the best prompt on the test set - for prompt in prompts: - # check if the combination of task, downstream_llm, seed and prompt has already been evaluated and is a row in - # the csv file - if Path(f"logs/{experiment_name}/best_scores.csv").exists(): - df = pd.read_csv(f"logs/{experiment_name}/best_scores.csv") - if ( - len( - df[ - (df["task"] == task_name) - & (df["downstream_llm"] == downstream_llm) - & (df["random_seed"] == random_seed) - & (df["prompt"] == prompt) - ] - ) - > 0 - ): - print(f"Prompt for task {task_name} has already been evaluated.") - continue - - else: - test_score = test_task.evaluate(prompt, test_predictor, subsample=True, n_samples=n_samples) - # save the test score to a csv file - df = pd.DataFrame( - { - "task": task_name, - "downstream_llm": downstream_llm, - "random_seed": random_seed, - "prompt": prompt, - "test_score": test_score, - }, - index=[0], - ) - df.to_csv(f"logs/{experiment_name}/best_scores.csv", mode="a", header=False, index=False) - - -def main(): - """Run experiment.""" - # read experiments - arg_parser = ArgumentParser() - arg_parser.add_argument("-e", "--experiment", type=str, help="Experiment Config Filepath") - args = arg_parser.parse_args() - all_configs = ConfigParser() - all_configs.read(args.experiment) - print(all_configs) - - experiment_name = all_configs["experiment"]["name"] - downstream_llms = all_configs["downstream_llm"]["name"].split(",") - tasks = all_configs["task"]["task_name"].split(",") - seed = int(all_configs["task"]["subsample_seed"]) - n_prompts = int(all_configs["task"]["n_prompts"]) - n_samples = int(all_configs["task"]["n_samples"]) - - logger.critical(f"Starting evaluation of best prompts for experiment {experiment_name}") - - for downstream_llm in downstream_llms: - logger.critical(f"Downstream LLM: {downstream_llm}") - - for task in tasks: - # sample initial prompt (read txt from datasets/cls/task_name/prompts.txt) - task_path = Path(f"data_sets/{task}/prompts.txt") - with open(task_path, "r", encoding="utf-8") as file: - lines = file.readlines() - lines = [line.strip() for line in lines] - # sample n_prompts prompts with seed - np.random.seed(seed) - prompts = np.random.choice(lines, n_prompts, replace=False) - - evaluate_prompts( - experiment_name=experiment_name, - downstream_llm=downstream_llm, - task_name=task, - random_seed=seed, - n_samples=n_samples, - prompts=prompts, - ) - print(task) - - logger.critical("Evaluation of best prompts finished.") - - -if __name__ == "__main__": - main() diff --git a/scripts/experiment_runs.py b/scripts/experiment_runs.py deleted file mode 100644 index fb17b25..0000000 --- a/scripts/experiment_runs.py +++ /dev/null @@ -1,133 +0,0 @@ -"""Experiments for paper Towards Cost-Effective Prompt Tuning.""" -from argparse import ArgumentParser -from configparser import ConfigParser -from logging import INFO, Logger -from pathlib import Path - -import numpy as np -import pandas as pd - -from promptolution.callbacks import BestPromptCallback, CSVCallback, ProgressBarCallback -from promptolution.config import Config -from promptolution.llms import get_llm -from promptolution.optimizers import get_optimizer -from promptolution.predictors import get_predictor -from promptolution.tasks import get_task -from promptolution.templates import EVOPROMPT_DE_TEMPLATE, EVOPROMPT_GA_TEMPLATE, EVOPROMPT_DE_TEMPLATE_TD, EVOPROMPT_GA_TEMPLATE_TD - -logger = Logger(__name__) -logger.setLevel(INFO) - - -def main(): - """Run the experiments defined in the provided configuration file.""" - # read experiments ini - arg_parser = ArgumentParser() - arg_parser.add_argument("-e", "--experiment", type=str, help="Experiment Config Filepath") - args = arg_parser.parse_args() - all_configs = ConfigParser() - all_configs.read(args.experiment) - experiment_name = all_configs["experiment"]["name"] - task_names = all_configs["task"]["task_name"].split(",") - meta_prompt_paths = all_configs["optimizer"]["meta_prompt_path"].split(",") - optimizer_names = all_configs["optimizer"]["name"].split(",") - meta_llms = all_configs["meta_llm"]["name"].split(",") - evaluator_llms = all_configs["evaluator_llm"]["name"].split(",") - downstream_llms = all_configs["downstream_llm"]["name"].split(",") - for task_name in task_names: - for optimizer_name, meta_prompt_path in zip(optimizer_names, meta_prompt_paths): - for evaluator_llm, meta_llm in zip(evaluator_llms, meta_llms): - for downstream_llm in downstream_llms: - for random_seed in [42, 47, 69]: - if "task_desc" in meta_prompt_path: - prompt_template = EVOPROMPT_DE_TEMPLATE_TD if "evopromptde" in optimizer_name else EVOPROMPT_GA_TEMPLATE_TD - else: - prompt_template = EVOPROMPT_DE_TEMPLATE if "evopromptde" in optimizer_name else EVOPROMPT_GA_TEMPLATE - config = Config( - task_name=task_name, - ds_path=f"data_sets/{task_name}", - n_steps=int(all_configs["task"]["steps"]), - optimizer=optimizer_name, - meta_llm=meta_llm, - downstream_llm=downstream_llm, - init_pop_size=int(all_configs["optimizer"]["init_population"]), - logging_dir=( - f"logs/{experiment_name}/" - + f"{task_name}_{optimizer_name}_{meta_llm}_{evaluator_llm}_{random_seed}.csv" - ), - experiment_name=experiment_name, - random_seed=random_seed, - evaluation_llm=evaluator_llm, - selection_mode="random", - donor_random=False, - meta_prompt=prompt_template, - ) - # skip already performed experiments - if Path(config.logging_dir).exists(): - continue - run_experiment(config) - - -def run_experiment(config: Config): - """Run a single experiment.""" - task = get_task(config, split="dev") - - init_populations = task.initial_population - # subsample using random seed - np.random.seed(config.random_seed) - init_population = np.random.choice(init_populations, config.init_pop_size, replace=False) - logger.critical(f"Task: {task.description}") - - predictor = get_predictor(config.evaluation_llm, classes=task.classes) - best_prompt_callback = BestPromptCallback() - callbacks = [ - # LoggerCallback(logger), - CSVCallback(config.logging_dir), - best_prompt_callback, - ProgressBarCallback(config.n_steps), - ] - prompt_template = config.meta_prompt - prompt_template = prompt_template.replace("", task.description) - - if "local" in config.meta_llm: - meta_llm = get_llm(config.meta_llm, batch_size=config.meta_bs) - else: - meta_llm = get_llm(config.meta_llm) - - optimizer = get_optimizer( - config, - meta_llm=meta_llm, - task=task, - initial_prompts=init_population, - callbacks=callbacks, - predictor=predictor, - ) - - logger.critical("🚨🚨🚨HEREEE WEEEE GOOO🚨🚨🚨") - optimizer.optimize(config.n_steps) - logger.critical("🎉We did it🥳") - best_prompt, best_score = best_prompt_callback.get_best_prompt() - logger.critical(f"Final prompt: {best_prompt}, with score: {best_score}") - - test_task = get_task(config.ds_path, split="test", random_seed=config.random_seed, task_name=config.task_name) - test_predictor = get_predictor(config.downstream_llm, classes=test_task.classes) - test_score = test_task.evaluate(best_prompt, test_predictor, subsample=False) - - # save test score to csv - df = pd.DataFrame( - { - "task": config.task_name, - "optimizer": config.optimizer, - "meta_llm": config.meta_llm, - "downstream_llm": config.downstream_llm, - "evaluation_llm": config.evaluation_llm, - "meta_prompt_path": config.meta_prompt_path, - "random_seed": config.random_seed, - "test_score": test_score, - }, - ) - df.to_csv(f"logs/{config.experiment_name}/best_scores.csv", mode="a", header=False, index=False) - - -if __name__ == "__main__": - main()