标题： 检索前先讨论：面向医疗问答的代理引导式对话以提升RAG效果 显示英文标题

标题： Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA

摘要： 医学问答（QA）是一项推理密集型任务，由于幻觉现象和过时的领域知识，对大型语言模型（LLMs）来说仍然具有挑战性。 检索增强生成（RAG）通过利用外部知识提供了一个有前景的微调解决方案。 然而，现有的医学RAG系统存在两个关键局限性：（1）缺乏对信息检索过程中类人推理行为的建模，以及（2）依赖次优的医学语料库，这通常会导致检索到无关或噪声片段。 为了解决这些挑战，我们提出了 讨论-RAG，这是一个即插即用的模块，旨在通过基于协作代理的推理来增强医学QA RAG系统。 我们的方法引入了摘要代理，该代理协调一组医学专家以模拟多轮头脑风暴，从而提高检索内容的相关性。 此外，决策代理会在最终整合之前评估检索到的片段。 在四个基准医学QA数据集上的实验结果显示，讨论-RAG始终优于MedRAG，在BioASQ上答案准确性提高了高达16.67%，在PubMedQA上提高了12.20%。 代码可在以下地址获取：https://github.com/LLM-VLM-GSL/Discuss-RAG。 显示英文摘要

摘要： Medical question answering (QA) is a reasoning-intensive task that remains challenging for large language models (LLMs) due to hallucinations and outdated domain knowledge. Retrieval-Augmented Generation (RAG) provides a promising post-training solution by leveraging external knowledge. However, existing medical RAG systems suffer from two key limitations: (1) a lack of modeling for human-like reasoning behaviors during information retrieval, and (2) reliance on suboptimal medical corpora, which often results in the retrieval of irrelevant or noisy snippets. To overcome these challenges, we propose Discuss-RAG, a plug-and-play module designed to enhance the medical QA RAG system through collaborative agent-based reasoning. Our method introduces a summarizer agent that orchestrates a team of medical experts to emulate multi-turn brainstorming, thereby improving the relevance of retrieved content. Additionally, a decision-making agent evaluates the retrieved snippets before their final integration. Experimental results on four benchmark medical QA datasets show that Discuss-RAG consistently outperforms MedRAG, especially significantly improving answer accuracy by up to 16.67% on BioASQ and 12.20% on PubMedQA. The code is available at: https://github.com/LLM-VLM-GSL/Discuss-RAG.