When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
It addresses the problem of inconsistent results in LLM self-correction for researchers and practitioners, highlighting that prior work may overestimate effectiveness due to flawed evaluations.
This paper critically surveys self-correction in large language models (LLMs), finding that successful self-correction typically requires reliable external feedback or large-scale fine-tuning, with prompted LLMs alone often failing except in exceptionally suited tasks.
Self-correction is an approach to improving responses from large language models (LLMs) by refining the responses using LLMs during inference. Prior work has proposed various self-correction frameworks using different sources of feedback, including self-evaluation and external feedback. However, there is still no consensus on the question of when LLMs can correct their own mistakes, as recent studies also report negative results. In this work, we critically survey broad papers and discuss the conditions required for successful self-correction. We first find that prior studies often do not define their research questions in detail and involve impractical frameworks or unfair evaluations that over-evaluate self-correction. To tackle these issues, we categorize research questions in self-correction research and provide a checklist for designing appropriate experiments. Our critical survey based on the newly categorized research questions shows that (1) no prior work demonstrates successful self-correction with feedback from prompted LLMs, except for studies in tasks that are exceptionally suited for self-correction, (2) self-correction works well in tasks that can use reliable external feedback, and (3) large-scale fine-tuning enables self-correction.