This repository is the official implementation of CriticEval, a comprehensive benchmark for evaluating critique ability of LLMs.

CriticEval: Evaluating Large Language Models as Critic
Tian Lan^1*, Wenwei Zhang^2*, Chen Xu¹, Heyan Huang¹, Dahua Lin², Kai Chen^2†, Xian-ling Mao^1† (^† Corresponding Author, ^* Equal Contribution)

¹ Beijing Institute of Technology, ² Shanghai AI Laboratory

[Dataset on HF] [Project Page] [Subjective LeaderBoard] [Objective LeaderBoard]

Critique ability are crucial in the scalable oversight and self-improvement of Large Language Models (LLMs). While many recent studies explore the critique ability of LLMs to judge and refine flaws in generations, how to comprehensively and reliably measure the critique abilities of LLMs is under-explored. This paper introduces CriticEval, a novel benchmark designed to comprehensively and reliably evaluate four key critique ability dimensions of LLMs: feedback, comparison, refinement and meta-feedback. CriticEval encompasses nine diverse tasks, each assessing the LLMs' ability to critique responses at varying levels of quality granularity. Our extensive evaluations of open-source and closed-source LLMs reveal intriguing relationships between the critique ability and tasks, response qualities, and model scales.

• [2024.9.25] CriticEval is accepted by NeurIPS 2024🥳🥳🥳
• [2024.2.21] Release paper, codes, data and other resources of CriticEval v1.3.

• Evaluate Qwen-1.5 series models
• Improve the reliability of subjective evaulation in CriticEval (v1.4)
• Expand to more diverse tasks
• Expand to Chinese applications
• Prepare and clean the codebase for OpenCompass
• Release the train set of CriticEval
• Support inference on Opencompass.

Download the dataset from huggingface dataset by running this command:

mkdir data
cd data
git clone https://huggingface.co/datasets/opencompass/CriticBench

which get into the data folder and clone the CriticEval dataset. Note that the human-annotated Likert scores, preference labels, and critiques in test set are excluded. You can submit your inference results on the test set (via run codes under inference folder) to this email. We will run your predictions and update the results in our leaderboard. Please also provide the scale of your tested model. The structure of your submission should be similar to that in example_data.

git clone https://github.com/open-compass/CriticEval.git
# prepare the env for evaluation toolkit
cd critic_bench
pip install -r requirements.txt
# prepare the env for LLM inference
cd ../inference
pip install -r requirements.txt

You need to inference LLMs to be evaluated on our proposed CriticEval, and generation results on CriticEval can be found in inference/outputs folder. If you are interested with our prompts for LLM, they are shown in inference/utils/prompts.py. Specifically, the inference code should be like:

# this line loads all the evaluation dataset in CriticEval from `inference/utils`
datasets = load_all_datasets(args['data_dir'])
# these lines init the tokenizer and models from huggingface
tokenizer = AutoTokenizer.from_pretrained(
    args['model_name'],
    trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    args['model_name'], 
    device_map="auto", 
    trust_remote_code=True
).cuda().eval()
...
# inference the LLM and save the results in json file format
for abbr, dataset in tqdm(datasets.items()):
    path = os.path.join(folder_path, abbr + ".json")
    results = {}
    for item in tqdm(dataset['dev']):        
        
        # If you want to inference other LLMs, please revise this line
        response, history = model.chat(tokenizer, item['question'], history=[])
            
        results[str(len(results))] = {
            'origin_prompt': item['question'],
            'prediction': response
        }
    # save the results into json file, with the abbr as the file name
    with open(path, 'w') as f:
        json.dump(results, f, ensure_ascii=False, indent=4)

We only provide the inference codebase for our InternLM2-7B-Chat, but it is easy to revise the inference code for evaluating your own LLMs (more details are in inference/internlm2.py).

We have already released the generation results of some representative LLMs on CriticEval, and you could found them in example_data/prediction_v1.3.tgz.

tar -xzvf example_data/prediction_v1.3.tgz

After unzipping, you could found the details of the predictions of LLMs on CriticEval. Typically, the format of the evaluation files are: {split}_{domain}_{dimension}_{format}.json, where split, dimension, and format are described above. The domain represents 9 task scenarios in our proposed CriticEval, consisting of translate, chat, qa, harmlessness, summary, math_cot, math_pot, code_exec, code_not_exec. Refer to more details in our paper. Here are some notes:

• the comp_feedback critique dimension always company with an reverse file which is used to address the well-known positional bias problem for LLM-as-a-judge procedure. Refer to more details in Section 4 of our paper.
• For feedback critique dimension, each domain has additional *_correction_part.json files, saving the evaluation results of critiques for the correct or the very high-quality responses. Refer to more details about these response in our paper.

The format of the evaluation result file is:

{
    '0': {
        'origin_prompt': 'The original prompt for LLMs to be evaluated',
        'prediction': 'The generated critiques to be evaluated'
    }
}

After getting the generation results under inference/outputs, your next step is to compute the objective and subjective scores in our proposed CriticEval using our toolkit. See more details about the objective and subjective scores in Section 4 of our paper.

We provide two ways for computing the objective and subjective scores in critic_bench folder.

• Objective scores could be computed automatically without any cost
• Subjective scores rely on the advanced GPT-4-turbo model for automatic evaluation

It is easy to compute the scores by running following commands.

Before running this code, please make sure that your own OpenAI API key in critic_bench/run.sh is set.

export OPENAI_API_KEY=...

Then, running the following codes for evaluation:

./run.sh <dimension> <format> <split> <save_dir>

• dimension denotes critique dimensions defined in our proposed CriticEval, which are feedback, correction, comp_feedback, and meta_feedback. Refer to more details about these critique dimensions in Section 2 of our paper.
• format denotes the critique format objective and subjective. Objective scores are spearman correlation, pass rate, preference accuracy that can be computed automatically without any cost, while subjective scores are computed by prompting GPT-4-turbo to compare generated critiques and our human-annotated high-quality critiques in CriticEval.
• split denotes the test or dev set to be evaluated.
• save_dir is any text path saving the evaluation results.

In run.sh file, you could find the corresponding commands for objective and subjective evaluation process. For example, for the feedback critique dimension, the objective evaluation is like:

python run_feedback.py --root_dir "../data/CriticEval" --prediction_dir "../example_data/prediction_v1.3" --split $3 --obj True

• root_dir contains the path of the test and dev set in CriticEval.
• prediction_dir contains the inference results of LLMs to be evaluated. We also provide the inference results of some representation LLMs in example_data. If you want to evaluate your own LLMs, please refer to inference/README.md for more details, and the prediction_dir could be set as ../inference/outputs.
• split denotes whether the test or the dev set is used.
• obj denotes that the objective evaluation is activated

For the subjective evaluation of the feedback critique dimension, the evaluation command is like:

python run_feedback.py --root_dir "../data/CriticEval" --prediction_dir "../example_data/prediction_v1.3" --evaluation_dir "../example_data/evaluation_v1.3/" --batch_size 1 --split $3 --obj False

• evaluation_dir saves the subjective evaluation scores of GPT-4, which can be re-loaded if the subjective evaluation process borke off. The order of the samples in each file in evaluation_dir follows the order in the original data in CriticEval (data/CriticEval).
• batch_size controls the number of the process for access GPT-4 API under multiprocessing setting.

The evaluation results of GPT-4 under save_dir is jsonl, and each line contains the evaluation results. The chain-of-thought evaluation results prompted by GPT-4 is in the evaluation key-value in each line, which is a dict consisting of the chain-of-thought rationale about GPT-4 (key-value cot) and a Likert score (key-value score) for each critiques, ranging from 1 to 10.

• 1 denotes the worst performance
• 10 denotes the best performance
• 8 denotes the comparable performance with our human-annotated high-quality critiques, and scores higher than 8 denotes the better performance of evaluated critiques.

To compute the overall score, please run this command:

# the results of GPT-4 on the test set
python compute_overall.py --mode obj --feedback_overall 63.54 --correction_overall 69.67 --comp_feedback_overall 57.33 --meta_feedback_overall 62.90

• replacing the mode with sub could compute the overall score for subjective evaluation
• ensure that at least one dimension's overall score is provided among the four dimensions

The subjective evaluation results of some representation LLMs are shown:

The objective evaluation results of some representation LLMs are shown:

Refer to our Project Page for the complete evaluation results on CriticEval.

CriticEval is built with OpenCompass. Thanks for their awesome work!

The quota for API-based LLMs are supported by Beijing Institute of Technology and Shanghai AI Laboratory. Thank you so much!

• Tian Lan: lantiangmftby@gmail.com
• Wenwei Zhang: zhangwenwei@pjlab.org.cn

@misc{lan2024criticevalevaluatinglargelanguage,
      title={CriticEval: Evaluating Large Language Model as Critic}, 
      author={Tian Lan and Wenwei Zhang and Chen Xu and Heyan Huang and Dahua Lin and Kai Chen and Xian-ling Mao},
      year={2024},
      eprint={2402.13764},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2402.13764}, 
}

This project is released under the Apache 2.0 license.