@inproceedings{zheng-etal-2025-critic,
title = "Critic-{C}o{T}: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought 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",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.89/",
doi = "10.18653/v1/2025.findings-acl.89",
pages = "1768--1806",
ISBN = "979-8-89176-256-5",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic
%A Zheng, Xin
%A Lou, Jie
%A Cao, Boxi
%A Wen, Xueru
%A Ji, Yuqiu
%A Lin, Hongyu
%A Lu, Yaojie
%A Han, Xianpei
%A Zhang, Debing
%A Sun, Le
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zheng-etal-2025-critic
%X 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.
%R 10.18653/v1/2025.findings-acl.89
%U https://aclanthology.org/2025.findings-acl.89/
%U https://doi.org/10.18653/v1/2025.findings-acl.89
%P 1768-1806
Markdown (Informal)
[Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic](https://aclanthology.org/2025.findings-acl.89/) (Zheng et al., Findings 2025)
ACL
- Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, and Le Sun. 2025. Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic. In Findings of the Association for Computational Linguistics: ACL 2025, pages 1768–1806, Vienna, Austria. Association for Computational Linguistics.