检索前先交流：基于智能体引导的讨论以改进医学问答中的RAG
Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
AI总结：
针对医学问答RAG系统缺乏类人推理建模及依赖次优语料库的问题，提出即插即用模块Discuss-RAG，通过多智能体协作推理提升检索质量，在多个基准上显著提升准确率。
摘要：
医学问答（QA）是一项推理密集型任务，由于幻觉和过时的领域知识，对大语言模型（LLMs）仍然具有挑战性。检索增强生成（RAG）通过利用外部知识，提供了一个有前途的训练后解决方案。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，这通常导致检索到不相关或有噪声的片段。为了克服这些挑战，我们提出了Discuss-RAG，这是一个即插即用模块，旨在通过基于智能体的协作推理来增强医学QA RAG系统。我们的方法引入了一个总结器智能体，它协调一组医学专家来模拟多轮头脑风暴，从而提高检索内容的相关性。此外，一个决策智能体在检索到的片段最终整合之前对其进行评估。在四个基准医学QA数据集上的实验结果表明，Discuss-RAG始终优于MedRAG，特别是在BioASQ上将答案准确率显著提高了16.67%，在PubMedQA上提高了12.20%。代码可在以下网址获取：https://github.com/LLM-VLM-GSL/Discuss-RAG。
英文摘要：
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(QA) is a reasoning-intensive task that remains challenging for large language models (LLMs) due to hallucinations and outdated domain knowledge.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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
