LLMs judge each other to improve medical question answering accuracy

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.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• DeepSeek-LLM-7B
• GPT OSS 20B
• HeadQA
• Llama 3.1:8b
• MedQA-USMLE
• Phi 4
• PubMedQA
• qwen2.5:7b

• paper
• model release

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