Recursive IntroSpEction

Recursive introspection in large language models (LLMs) focuses on enhancing their ability to self-correct and improve their responses iteratively, mimicking human-like reflection and refinement of thought processes. Current research explores methods like iterative fine-tuning and novel prompting strategies, often drawing inspiration from reinforcement learning and imitation learning principles, to enable LLMs to detect and rectify errors in their reasoning. This research aims to improve LLMs' performance on complex tasks requiring multi-step reasoning and robust handling of uncertainty, with potential applications in areas like human-robot interaction and more reliable AI systems.