LLM Self-Correction
LLM: Large Language Model
Overview
This field asks whether language models can recognize faulty claims, reasoning steps, or actions and revise them, which matters for reliability in tasks such as mathematics, coding, question answering, and clinical workflows. Approaches include prompted reflection and critique, feedback from code execution or formal verifiers, retrieval and multi-agent review, training with reinforcement learning on failed attempts, and signals from model uncertainty or internal representations. Evaluations use settings such as GSM8K and AIME for mathematics, HumanEval and SWE-bench for code, and Text-to-SQL benchmarks, often tracking both errors fixed and correct answers damaged. A central tension is that detecting an error does not ensure a useful repair: noisy feedback, poor error localization, or repeated revision can worsen outputs, motivating selective, evidence-grounded correction and reliable stopping rules.
Latest papers
159 papers24 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.