@inproceedings{zhang-etal-2025-understanding,
title = "Understanding the Dark Side of {LLM}s' Intrinsic Self-Correction",
author = "Zhang, Qingjie and
Wang, Di and
Qian, Haoting and
Li, Yiming and
Zhang, Tianwei and
Huang, Minlie and
Xu, Ke and
Li, Hewu and
Yan, Liu and
Qiu, Han",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1314/",
doi = "10.18653/v1/2025.acl-long.1314",
pages = "27066--27101",
ISBN = "979-8-89176-251-0",
abstract = "Intrinsic self-correction was initially proposed to improve LLMs' responses via feedback solely based on their inherent capability. However, recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback. In this paper, our research goal is to *interpret LLMs' intrinsic self-correction for different tasks, especially for those failure cases.* By including one simple task and three complex tasks with state-of-the-art (SOTA) LLMs like ChatGPT, Llama, and DeepSeek, we design three interpretation methods to reveal the dark side of LLMs' intrinsic self-correction. We identify intrinsic self-correction can (1) cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions; (2) introduce human-like cognitive bias on complex tasks. In light of our findings, we also provide two simple yet effective strategies for alleviation: question repeating and supervised fine-tuning with a few samples. We open-source our work at https://x-isc.info/."
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<abstract>Intrinsic self-correction was initially proposed to improve LLMs’ responses via feedback solely based on their inherent capability. However, recent works show that LLMs’ intrinsic self-correction fails without oracle labels as feedback. In this paper, our research goal is to *interpret LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.* By including one simple task and three complex tasks with state-of-the-art (SOTA) LLMs like ChatGPT, Llama, and DeepSeek, we design three interpretation methods to reveal the dark side of LLMs’ intrinsic self-correction. We identify intrinsic self-correction can (1) cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions; (2) introduce human-like cognitive bias on complex tasks. In light of our findings, we also provide two simple yet effective strategies for alleviation: question repeating and supervised fine-tuning with a few samples. We open-source our work at https://x-isc.info/.</abstract>
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%0 Conference Proceedings
%T Understanding the Dark Side of LLMs’ Intrinsic Self-Correction
%A Zhang, Qingjie
%A Wang, Di
%A Qian, Haoting
%A Li, Yiming
%A Zhang, Tianwei
%A Huang, Minlie
%A Xu, Ke
%A Li, Hewu
%A Yan, Liu
%A Qiu, Han
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhang-etal-2025-understanding
%X Intrinsic self-correction was initially proposed to improve LLMs’ responses via feedback solely based on their inherent capability. However, recent works show that LLMs’ intrinsic self-correction fails without oracle labels as feedback. In this paper, our research goal is to *interpret LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.* By including one simple task and three complex tasks with state-of-the-art (SOTA) LLMs like ChatGPT, Llama, and DeepSeek, we design three interpretation methods to reveal the dark side of LLMs’ intrinsic self-correction. We identify intrinsic self-correction can (1) cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions; (2) introduce human-like cognitive bias on complex tasks. In light of our findings, we also provide two simple yet effective strategies for alleviation: question repeating and supervised fine-tuning with a few samples. We open-source our work at https://x-isc.info/.
%R 10.18653/v1/2025.acl-long.1314
%U https://aclanthology.org/2025.acl-long.1314/
%U https://doi.org/10.18653/v1/2025.acl-long.1314
%P 27066-27101
Markdown (Informal)
[Understanding the Dark Side of LLMs’ Intrinsic Self-Correction](https://aclanthology.org/2025.acl-long.1314/) (Zhang et al., ACL 2025)
ACL
- Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Liu Yan, and Han Qiu. 2025. Understanding the Dark Side of LLMs’ Intrinsic Self-Correction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27066–27101, Vienna, Austria. Association for Computational Linguistics.