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arXiv · 2412.14959

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·Yan Liu·Han Qiu
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Abstract

Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback prompts. In this paper, we aim 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 families (o1, 4o, 3.5-turbo) and Llama families (2-7B, 3-8B, and 3.1-8B), 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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Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Yan Liu, Han Qiu. 2024-12-19. Understanding the Dark Side of LLMs' Intrinsic Self-Correction. https://arxiv.org/abs/2412.14959

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