Large language models (LLMs)  have demonstrated remarkable performance across a wide array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent behaviors, including hallucination, unfaithful reasoning, and toxic content. A promising approach to rectify these flaws isself-correction,
where the LLM itself is prompted or guided to fix problems in its own output.
Techniques leveragingautomated feedback—either produced by the LLM itself or some external system—are of particular interest as they are a promising way to make LLM-based solutions more practical and deployable with minimal human feedback.
This paper presents a comprehensive review of this emerging class of techniques.
We analyze and taxonomize a wide array of recent work utilizing these strategies, including training-time, generation-time, and post-hoc correction. We also summarize the major applications of this strategy and conclude by discussing future directions and challenges.