The code for the paper "ECR-Chain: Advancing Generative Language Models to Better Emotion Cause Reasoners through Reasoning Chains" (IJCAI-2024).
The Appendix mentioned in the paper is present in here.
The code of "Supervised CEE" part is based on this version of lm-sys/FastChat.
- python==3.10
- torch==1.13.1+cu116
- transformers==4.28.1
- fschat==0.2.15
- install from source following the instructions.
- deepspeed==0.9.0
- peft==0.4.0.dev0
- openai==0.27.8
The original RECCON dataset can be found at this link.
We preprocessed the original RECCON-DD dataset and saved on RECCON_all_data.json. Note that there are some repetitive positive causal pairs in the original dataset, which are removed in our preprocessing process, following the KBCIN.
Follow the code or instructions in chatgpt/chatgpt_generation_for_construction.py to perform the 4-stage process on the RECCON-DD training set and construct the ECR-Chain Set.
Our constructed ECR-Chain Set can be found at here.
Run the code chatgpt/chatgpt_generation_for_fewshot_cee.py to perform few-shot prompting with ECR-Chain on ChatGPT.
You need to modify template_name in the above code if use other prompts:
- Baseline Few-shot Prompt (directly predict the answer):
CEE_fewshot - ECR-Chain:
CEE_backward_v2_fewshot - ECR-Chain (without theme):
CEE_backward_v3_fewshot - ECR-Chain (without theme and reactions):
CEE_backward_v4_fewshot - ECR-Chain (without reactions):
CEE_backward_v5_fewshot - ECR-Chain (only with stimuli):
CEE_stimuli_fewshot
- Answer:
EmoReasonLlama/scripts/lora_train_answer.sh - Reasoning:
EmoReasonLlama/scripts/lora_train_reasoning.sh - Multi-task:
EmoReasonLlama/scripts/lora_train_multitask.sh
- Answer:
EmoReasonLlama/scripts/inference_answer.sh - Reasoning:
EmoReasonLlama/scripts/inference_reasoning.sh
Compare and analyze the quality of reasoning chains generated by two models via GPT-4. First save the two models' generated chains into json file and then get the GPT-4 analysis results via gpt4_eval/gpt4_generation.py.