BioRAGent用大模型增强生物医学问答，支持可编辑的查询扩展与溯源。
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
- 利用大模型进行查询扩展和片段提取，提升搜索精度。
- 在BioASQ 2024挑战中表现优异，验证少样本学习有效性。
- 交互式网页系统，支持带引用的段落回复，适合科研人员使用。
我们提出BioRAGent，一个面向生物医学问答的交互式网络版检索增强生成（RAG）系统。该系统利用大语言模型（LLMs）实现查询扩展、摘要片段提取和答案生成，并通过源文献引用链接保持透明性，同时展示生成的查询供用户编辑。基于我们在BioASQ 2024挑战中的成功经验，展示了大模型在专业搜索场景下少样本学习的有效应用。系统支持直接短段落回答及带内嵌引用的回答形式。演示地址已公开，源代码可通过GitHub获取。
原文摘要 · Abstract (English)
We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query expansion, snippet extraction, and answer generation while maintaining transparency through citation links to the source documents and displaying generated queries for further editing. Building on our successful participation in the BioASQ 2024 challenge, we demonstrate how few-shot learning with LLMs can be effectively applied for a professional search setting. The system supports both direct short paragraph style responses and responses with inline citations. Our demo is available online, and the source code is publicly accessible through GitHub.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。