Discuss-RAG
This paper introduces Discuss-RAG, a plug-and-play agent-based framework that enhances retrieval-augmented generation (RAG) for medical question answering by mimicking human-like clinical reasoning. Standard RAG systems rely on embedding-based retrieval and lack mechanisms to verify relevance or logical coherence, often leading to hallucinations or outdated answers. Discuss-RAG addresses these gaps via a modular agent setup that simulates multi-turn medical discussions and performs post-retrieval verification. Key ideas:
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Multi-agent collaboration: A summarizer agent orchestrates a team of medical domain experts who iteratively refine a contextual summary through simulated brainstorming, providing deeper and more structured information to guide retrieval.
Decision-making agent: After retrieval, a verifier and a decision-making agent assess snippet quality and trigger fallback strategies when relevance is low, improving answer accuracy and contextual grounding.
Plug-and-play design: Discuss-RAG is training-free and modular, allowing easy integration into existing RAG pipelines.
Strong performance gains: Across four benchmarks, Discuss-RAG outperforms MedRAG with substantial accuracy improvements, notably +16.67% on BioASQ and +12.20% on PubMedQA.