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RealCritic
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# RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques <p align="center" width="100%"> <a ><img src="./imgs/RealCritic.png" alt="RealCritic" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a> </p> RealCritic is a novel benchmark for evaluating language models' critique capabilities through a closed-loop, effectiveness-driven approach. Unlike traditional open-loop evaluations, we measure critique quality based on its ability to drive solution improvement. ## Key Contributions 1. **Closed-loop Evaluation**: Measures critique quality through improvement in refined solutions rather than isolated judgments 2. **Comprehensive Assessment**: First benchmark to evaluate self-critique, cross-critique, and iterative critique capabilities 3. **Novel Findings**: Reveals significant gaps between reasoning-based and traditional LLMs in critique abilities ## Benchmark Details ### Tasks - ARC Challenge: Multiple choice science questions - GSM8K: Grade school math problems - MATH: Advanced mathematics - College Math: Undergraduate mathematics - Minerva Math: Scientific reasoning - GPQA: Physics questions - MMLU STEM: Science/tech/engineering/math topics - Olympiad Bench: Competition-level problems ### Evaluation Modes - **Direct CoT**: Baseline problem-solving ability - **Self-Critique**: Model critiques its own solutions - **Cross-Critique**: Model critiques other models' solutions - **Iterative Critique**: Multi-round refinement process ## Usage ### Installation Please follow the installation instructions in the [Qwen2.5-Math repository](https://github.com/QwenLM/Qwen2.5-Math.git). ### Local Evaluation ```bash #!/bin/bash PROMPT_TYPE="critic_and_correct" USER_PROMPT_TYPE="critic_and_correct" MODEL_NAME_OR_PATH="path/to/your/model" DATA_DIR="data/mix_data" DATASETS="arc-challenge,college_math,gpqa,gsm8k,math,minerva_math,mmlu_stem,olympiadbench" MULTI_TURN=1 export CUDA_VISIBLE_DEVICES="0,1" bash sh/local/run_cross_critic.sh $PROMPT_TYPE $USER_PROMPT_TYPE $MODEL_NAME_OR_PATH $DATA_DIR $DATASETS $MULTI_TURN # bash sh/local/run_self_critic.sh $PROMPT_TYPE $USER_PROMPT_TYPE $MODEL_NAME_OR_PATH $DATA_DIR $DATASETS $MULTI_TURN # bash sh/local/run_direct_cot.sh $PROMPT_TYPE $USER_PROMPT_TYPE $MODEL_NAME_OR_PATH $DATA_DIR $DATASETS ``` ### API Evaluation ```bash #!/bin/bash PROMPT_TYPE="direct-cot" USER_PROMPT_TYPE="default-cot" MODEL_NAME_OR_PATH="gpt-4o" DATA_DIR="data/mix_data" DATASETS="arc-challenge,college_math,gpqa,gsm8k,math,minerva_math,mmlu_stem,olympiadbench" MULTI_TURN=1 export DASHSCOPE_API_KEY="your_api_key" export DASHSCOPE_BASE_URL="https://api.dashscope.com/v1" bash sh/run_direct_cot.sh $PROMPT_TYPE $USER_PROMPT_TYPE $MODEL_NAME_OR_PATH $DATA_DIR $DATASETS # bash sh/run_cross_critic.sh $PROMPT_TYPE $USER_PROMPT_TYPE $MODEL_NAME_OR_PATH $DATA_DIR $DATASETS $MULTI_TURN # bash sh/run_self_critic.sh $PROMPT_TYPE $USER_PROMPT_TYPE $MODEL_NAME_OR_PATH $DATA_DIR $DATASETS $MULTI_TURN ``` ### Post Check (Optional) ``` python3 -u model_eval_critic.py \ --data_dir path/for/critic/math_eval \ --model_name_or_path /post/check/model \ --output_dir /output/dir/ \ --data_name ${DATA_NAME} \ --seed 0 \ --temperature 0 \ --top_p 1 \ ``` ### Customizing Prompts All prompts are defined in `utils.py`: ```python # System prompts SYSTEM_PROMPT_TEMPLATES = { "direct-cot": "...", "critic_and_correct": "...", # Must include "critic" keyword } # User prompts USER_PROMPT_TEMPLATE = { "default-cot": { "type": "cot", "template": "..." }, "critic_and_correct": { "type": "critic", "template": "..." } } ``` ## Acknowledgements The code in this repository is built upon [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math.git). We thank the Qwen team for their excellent work and open-source contributions. ## Citation If you find this work useful, please cite: ```bibtex @article{tang2024realcritic, title={RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques}, author={Tang, Zhengyang and Li, Ziniu and Xiao, Zhenyang and Ding, Tian and Sun, Ruoyu and Wang, Benyou and Liu, Dayiheng and Huang, Fei and Liu, Tianyu and Yu, Bowen and Lin, Junyang}, journal={arXiv preprint arXiv:2501.14492}, year={2024} } ```