BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
This work addresses the problem of efficient and transparent scientific Q&A for biomedical researchers, though it appears incremental as it builds on existing RAG and LLM methods.
The authors tackled biomedical question answering by developing BioRAGent, a retrieval-augmented generation system that uses large language models for query expansion and answer generation, resulting in a publicly available web-based demo and source code.
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.