Let LLMs Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering
Researchers have developed a novel multi-agent system where large language models (LLMs) act as both problem solvers and peer reviewers to improve medical question answering. This method involves multiple LLM agents generating reasoning chains and then evaluating each other's logic for accuracy and soundness. Experiments using five LLMs on three benchmark datasets demonstrated that this peer-reviewed reasoning approach consistently outperformed single-model reasoning and majority voting, achieving a top accuracy of 0.820. AI
IMPACT This multi-agent peer-review system enhances LLM accuracy and interpretability in specialized domains like medical question answering.