📄 Paper | 🤗 Dataset | 📝 Citation
Self-critic has become a crucial mechanism for enhancing the reasoning performance of LLMs. However, current approaches mainly involve basic prompts for intuitive instance-level feedback, which resembles System-1 processes and limits the reasoning capabilities. Moreover, there is a lack of in-depth investigations into the relationship between LLM's ability to criticize and its task-solving performance. To address these issues, we propose Critic-CoT, a novel framework that pushes LLMs toward System-2-like critic capability. Through a step-wise CoT reasoning paradigm and the automatic construction of weak-supervision data without human annotation, Critic-CoT enables LLMs to engage in slow, analytic self-critique and refinement, thereby improving their reasoning abilities. Experiments on GSM8K and MATH and out-of-domain evaluation demonstrate that our enhanced model significantly boosts task-solving performance by filtering out invalid solutions or iterative refinement. Furthermore, we investigate the intrinsic correlation between critique and task-solving abilities within LLMs, discovering that these abilities can mutually reinforce each other rather than conflict.
@article{zheng2025critic,
title={Critic-cot: Boosting the reasoning abilities of large language model via chain-of-thoughts critic},
author={Zheng, Xin and Lou, Jie and Cao, Boxi and Wen, Xueru and Ji, Yuqiu and Lin, Hongyu and Lu, Yaojie and Han, Xianpei and Zhang, Debing and Sun, Le},
booktitle={Findings of the Association for Computational Linguistics: ACL 2025},
publisher={Association for Computational Linguistics},
year={2025}
}