Evaluating Large Language Models at Evaluating Instruction Following

This repository contains the data and code for paper Evaluating Large Language Models at Evaluating Instruction Following. In this paper, we introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. LLMBar consists of 419 instances, where each entry contains an instruction paired with two outputs: one faithfully and correctly follows the instruction and the other deviates from it. There is also a gold preference label indicating which output is objectively better for each instance.

Quick Links

• Requirements
• Data
• Hugging Face Datasets
• Code Structure
• Run LLM Evaluators
• Bug or Questions?
• Citation

Requirements

Please install the packages by pip install -r requirements.txt. This codebase has been tested with Python 3.10.4.

Data

All the data are stored in Dataset/. The Natural set of LLMBar is stored in Dataset/Natural. The four subsets of LLMBar Adversarial set are stored in Dataset/LLMBar/Adversarial/{Neighbor, GPTInst, GPTOut, Manual}.

The five evaluation subsets we studied in 4.6 Case Study: A More Challenging Meta-Evaluation Set are stored in Dataset/CaseStudy/{Constraint, Negation, Normal, Base_9, Base_10}.

We also evaluate LLM evaluators on FairEval, LLMEval $^2$, and MT-Bench. We remove LLMEval $^2$ instances whose instructions are empty or non-English and add the task description before the raw input to get the instruction. For MT-Bench, we get the gold preferences by majority vote. We remove all ``TIE'' instances and randomly sample 200 instances for LLMEval $^2$ and MT-Bench respectively. The processed data are stored in Dataset/Processed/{FairEval, LLMEval^2, MT-Bench}.

All the evaluation instances in each folder are stored in dataset.json. Each instance is a JSON object with the format:

{
    "input": "Infer the implied meaning of the following sentence: She is not what she used to be.",
    "output_1": "She is not as she once was.",
    "output_2": "She has changed substantially over time.",
    "label": 2
}

"input" is the input instruction. "output_1" and "output_2" are the two evaluated outputs $O_1$ and $O_2$ respectively. label is either 1 or 2, indicating which output is objectively better.

Hugging Face Datasets

Our dataset is now available on Hugging Face Datasets! You can access and utilize it using the 🤗 Datasets library.

from datasets import load_dataset
LLMBar = load_dataset("princeton-nlp/LLMBar", "LLMBar")
CaseStudy = load_dataset("princeton-nlp/LLMBar", "CaseStudy")

Code Structure

All the codes are stored in LLMEvaluator/.

• evaluate.py: run file to reproduce our baselines.
• evaluators/config: folder that contains all config files to reproduce baselines.
• evaluators/prompts: folder that contains all prompt files.

Run LLM Evaluators

You can reproduce LLM evaluators from our paper by

cd LLMEvaluator

python evaluate.py \
    --path {path_to_data_folder} \
    --evaluator {base_llm}/{prompting_strategy} \
    --num_procs {number_of_processes}
    # The default value of num_procs is 10
    # See the following content for more arguments

{base_llm} is one of GPT-4, ChatGPT, LLaMA2, PaLM2, Falcon, and ChatGPT-0301.

• If you use GPT-4, ChatGPT, or ChatGPT-0301, you will also need to pass the OpenAI API arguments:

  --api_type {your_api_type} \
  --api_version {your_api_version} \
  --api_base {your_api_base} \
  --api_key {your_api_key}
  --organization {your_organization}
  # If you use Azure API, you may need to pass api_type, api_version, api_base, and api_key.
  # Otherwise, you may need to pass api_key and organization.

  Also, ensure that the arguments in the config files align with those expected by the function.
• If you use PaLM2, you will also need to pass the PaLM API key:

  --palm_api_key {your_palm_api_key}

• If you use LLaMA2 (LLaMA-2-70B-Chat) or Falcon (Falcon-180B-Chat), you will also need to pass the Hugging Face authorization token (please make sure your account has the access to the model):

  --hf_use_auth_token {your_use_auth_token}

An example of the command:

python evaluate.py \
    --path ../Dataset/LLMBar/Natural \
    --evaluator GPT-4/Vanilla \
    --api_type azure \
    --api_version 2023-05-15 \
    --api_base {your_api_base} \
    --api_key {your_api_key}

The current list of prompting_strategy (check out our paper for more details) includes:

• Vanilla_NoRules: Vanilla
• Vanilla: Vanilla* (Vanilla+Rules)
• Vanilla_1shot: Vanilla* (Vanilla+Rules) w/ 1-shot in-context learning
• Vanilla_2shot: Vanilla* (Vanilla+Rules) w/ 2-shot in-context learning
• CoT: CoT* (CoT+Rules)
• Metrics: Metrics* (Rules+Metrics)
• Reference: Reference* (Rules+Reference)
• Metrics_Reference: Metrics+Reference* (Rules+Metrics+Reference)
• Swap: Swap* (Rules+Swap)
• Swap+CoT: Swap+CoT* (Rules+Swap+CoT)
• Rating_NoRules: Vanilla w/ the rating approach
• Rating: Vanilla* (Vanilla+Rules) w/ the rating approach
• Rating_Metrics: Metrics* (Rules+Metrics) w/ the rating approach
• Rating_Reference: Reference* (Rules+Reference) w/ the rating approach
• Rating_Metrics_Reference: Metrics+Reference* (Rules+Metrics+Reference) w/ the rating approach

After running the code, the results will be stored in {path_to_data_folder}/evaluators/{base_llm}/{prompting_strategy}. result.json is the intermediate results for evaluating the LLM evaluators on all instances. statistics.json is the final statistics of the evaluation, where "correct_average" and equal represent average accuracy (Acc.) and positional agreement rate (Agr.) respectively. We have already put our results (reported in our paper) in the repository.

Bug or Questions?

If you have any questions related to the code or the paper, feel free to email Zhiyuan Zeng (zhiyuan1zeng@gmail.com or zengzy20@mails.tsinghua.edu.cn). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

Citation

Please cite our paper if you use this repo in your work:

@inproceedings{zeng2024llmbar,
   title={Evaluating Large Language Models at Evaluating Instruction Following},
   author={Zeng, Zhiyuan and Yu, Jiatong and Gao, Tianyu and Meng, Yu and Goyal, Tanya and Chen, Danqi},
   booktitle = {International Conference on Learning Representations (ICLR)},
   year={2024}
}