A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
Bioragent: A retrieval-augmented gener- ation system for showcasing generative query expansion and domain- specific search for scientific q&a
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abstract
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.
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A question-type-specific LLM ensemble and multi-agent pipeline achieved competitive results on BioASQ 14b Task B, including first place in the factoid subtask of Batch 4.
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From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
A question-type-specific LLM ensemble and multi-agent pipeline achieved competitive results on BioASQ 14b Task B, including first place in the factoid subtask of Batch 4.