Understanding the Dark Side of LLMs’ Intrinsic Self-Correction
Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li
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/.
BibTeX
@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"
}