Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic
<|reference_start|>Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic: 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 distant-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 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.<|reference_end|>
@article{zheng2024critic-cot:,
    title={Critic-CoT: Boosting the reasoning abilities of large language model via
  Chain-of-thoughts Critic},
    author={Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie
  Lu, Xianpei Han, Debing Zhang, Le Sun},
    journal={arXiv preprint arXiv:2408.16326},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16326},
    primaryClass={cs.CL}
}