  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 in
general tasks, (2) self-correction works well in tasks that can use reliable
external feedback, and (3) large-scale fine-tuning enables self-correction.
