arXiv 2510.157822025-10-17cs.AI

Demo: Guide-RAG：面向长新冠检索增强生成的知识库证据驱动策展

Demo: Guide-RAG: Evidence-Driven Corpus Curation for Retrieval-Augmented Generation in Long COVID

Department of Computer Science University of Texas at Austin（计算机科学系得克萨斯大学奥斯汀分校）；School of Information University of Texas at Austin（信息学院得克萨斯大学奥斯汀分校）；McCombs School of Business University of Texas at Austin（麦库姆斯商学院得克萨斯大学奥斯汀分校）；Dell Medical School University of Texas at Austin（德克萨斯大学奥斯汀分校戴尔医学学院）

Philip DiGiacomo, Haoyang Wang, Jinrui Fang, Yan Leng, W Michael Brode, Ying Ding

备注：: Accepted to 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance

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AI总结：

针对长新冠临床问答，提出Guide-RAG系统，结合临床指南与高质量系统综述的RAG知识库配置，优于单一指南和大规模文献库，实现信息平衡与有效决策支持。

摘要：

随着AI聊天机器人在临床医学中的日益普及，为复杂的新发疾病开发有效的框架面临重大挑战。我们针对长新冠（LC）临床问答开发并评估了六种检索增强生成（RAG）知识库配置，范围从专家策展来源到大规模文献数据库。我们的评估采用LLM作为评判者的框架，在忠实性、相关性和全面性指标上使用LongCOVID-CQ（一个由专家生成的临床问题组成的新数据集）。我们结合临床指南与高质量系统综述的RAG知识库配置，始终优于狭窄的单一指南方法和大规模文献数据库。我们的研究结果表明，对于新发疾病，基于策展的二次综述的检索在狭窄的共识文档和未过滤的原始文献之间提供了最佳平衡，支持临床决策，同时避免信息过载和过度简化的指导。我们提出了Guide-RAG，一个聊天机器人系统及配套评估框架，它整合了策展的专家知识和全面的文献数据库，以有效回答LC临床问题。

英文摘要：

As AI chatbots gain adoption in clinical medicine, developing effective frameworks for complex, emerging diseases presents significant challenges. We developed and evaluated six Retrieval-Augmented Generation (RAG) corpus configurations for Long COVID (LC) clinical question answering, ranging from expert-curated sources to large-scale literature databases. Our evaluation employed an LLM-as-a-judge framework across faithfulness, relevance, and comprehensiveness metrics using LongCOVID-CQ, a novel dataset of expert-generated clinical questions. Our RAG corpus configuration combining clinical guidelines with high-quality systematic reviews consistently outperformed both narrow single-guideline approaches and large-scale literature databases. Our findings suggest that for emerging diseases, retrieval grounded in curated secondary reviews provides an optimal balance between narrow consensus documents and unfiltered primary literature, supporting clinical decision-making while avoiding information overload and oversimplified guidance. We propose Guide-RAG, a chatbot system and accompanying evaluation framework that integrates both curated expert knowledge and comprehensive literature databases to effectively answer LC clinical questions.

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