Large Language Models have Intrinsic Self-Correction Ability

Dancheng Liu, Amir Nassereldine, Chenhui Xu, Ziming Yang, Yuting Hu, Jiajie Li, Utkarsh Kumar, Changjae Lee, Ruiyang Qin, Yiyu Shi, Jinjun Xiong

Keywords: large language model, self correction, prompt engineering, foundation model

TL;DR: Different from the previous perspective, self-correction is an intrinsic ability of an LLM that can be utilized through correct temperature and prompt to enhance its performance.

Abstract: Large language models (LLMs) have attracted significant attention for their exceptional abilities in various natural language processing tasks, but they suffer from hallucinations that will cause performance degradation. One promising solution to improve the LLMs' performance is to ask LLMs to revise their answer after generation, a technique known as self-correction. Among the two types of self-correction, intrinsic self-correction is considered a promising direction because it does not utilize external knowledge. However, recent works doubt the validity of LLM's ability to conduct intrinsic self-correction. In this paper, we present a novel perspective on the intrinsic self-correction capabilities of LLMs through theoretical analyses and empirical experiments. In addition, we identify two critical factors for successful self-correction: zero temperature and fair prompts. Leveraging these factors, we demonstrate that intrinsic self-correction ability is exhibited across multiple existing LLMs. Our findings offer insights into the fundamental theories underlying the self-correction behavior of LLMs and remark on the importance of unbiased prompts and zero temperature settings in harnessing their full potential.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4774