Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

Understand

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 is self-correction, where the LLM itself is prompted or guided to fix problems in its own output.
• Techniques leveraging automated 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.