Summary of Bioragent: a Retrieval-augmented Generation System For Showcasing Generative Query Expansion and Domain-specific Search For Scientific Q&a, by Samy Ateia et al.
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
by Samy Ateia, Udo Kruschwitz
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system, uses large language models (LLMs) for query expansion, snippet extraction, and answer generation in biomedical question answering. The system maintains transparency by linking to source documents and allowing users to edit generated queries. This system builds on our successful participation in the BioASQ 2024 challenge, demonstrating the effectiveness of few-shot learning with LLMs in a professional search setting. BioRAGent supports both short paragraph responses and responses with inline citations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BioRAGent is a new way to find answers to biomedical questions online. It uses big language models to help you ask better questions, find relevant information, and get accurate answers. The system makes sure you can see where the answers come from by linking to original sources. This helps make searching for medical information more reliable and helpful. |
Keywords
» Artificial intelligence » Few shot » Question answering » Rag » Retrieval augmented generation
