用多智能体系统提升基因组问答能力，性能比现有方法高12%。
From Single to Multi-Agent Reasoning: Advancing GeneGPT for Genomics QA
- 设计多智能体架构，动态协调专业代理处理复杂基因组问题。
- 在9个任务上平均性能超越GeneGPT 12%，最高提升达18%。
- 框架可拓展至其他需专家知识提取的科学领域，通用性强。
理解基因组信息对生物医学研究至关重要，但从复杂的分布式数据库中提取数据仍具挑战。大语言模型（LLMs）虽有潜力用于基因组问答（QA），却受限于对领域专用数据库的访问。GeneGPT是当前最先进的系统，通过调用专业API增强LLM能力，但其受限于僵化的API依赖和适应性差。我们复现了GeneGPT，并提出GenomAgent——一个高效协调专业化代理的多智能体框架，以应对复杂基因组查询。在GeneTuring基准的9项任务上评估，GenomAgent平均性能优于GeneGPT 12%，其灵活架构还可扩展至需要专家知识提取的多种科学领域。
原文摘要 · Abstract (English)
Comprehending genomic information is essential for biomedical research, yet extracting data from complex distributed databases remains challenging. Large language models (LLMs) offer potential for genomic Question Answering (QA) but face limitations due to restricted access to domain-specific databases. GeneGPT is the current state-of-the-art system that enhances LLMs by utilizing specialized API calls, though it is constrained by rigid API dependencies and limited adaptability. We replicate GeneGPT and propose GenomAgent, a multi-agent framework that efficiently coordinates specialized agents for complex genomics queries. Evaluated on nine tasks from the GeneTuring benchmark, GenomAgent outperforms GeneGPT by 12% on average, and its flexible architecture extends beyond genomics to various scientific domains needing expert knowledge extraction.
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