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相关实验视频

Updated: Jul 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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基于大型语言模型的多代理合作,用于抽象选,以实现自动化系统审查.

Opeoluwa Akinseloyin1, Xiaorui Jiang2, Vasile Palade1

  • 1Centre for Computational Science and Mathematical Modelling, Coventry University, Puma Way, Coventry, CV1 2TT, United Kingdom.

Biology methods & protocols
|March 4, 2026
PubMed
概括

多个大型语言模型 (LLM) 的协作显著提高了系统审查抽象选效率. 多数投票成为首要策略,减少了高达68%的工作量,同时保持了高回忆率.

关键词:
抽象的选抽象的选总的来说,一个团队就是一个团队.大型语言模型多代理系统是多代理系统.系统性审查 系统性审查

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科学领域:

  • 医疗保健中的人工智能
  • 信息科学 信息科学 信息科学
  • 生物医学信息学 生物医学信息学

背景情况:

  • 系统性审查 (SRs) 对基于证据的实践至关重要,但耗时,特别是抽象选.
  • 目前的抽象选方法是劳动密集型的,阻碍了研究证据的高效合成.

研究的目的:

  • 评估多个大型语言模型 (多个LLM) 协作在提高系统审查抽象选效率和降低成本方面的有效性.
  • 将不同的多LLM协作策略与基线问答 (QA) 方法进行比较.

主要方法:

  • 抽象选是以质量保证任务为模式,使用具有成本效益的LLMs.
  • 测试了三个多个LLM协作策略:多数投票,多个代理商辩论和基于LLM的裁决.
  • 从CLEF eHealth 2019基准中对28个SR进行了绩效评估,使用了平均平均精度 (MAP) 和在采样中节省的工作 (WSS@95%) 等指标.

主要成果:

  • 多LLM合作显著超过了个别质量保证基线.
  • 多数投票实现了最高的MAP (0.462和0.341) 和WSS@95% (0.606和0.680),表明在95%的回忆中,潜在的工作量减少了高达68%.
  • 多代理辩论对较弱的模型有好处,而基于LLM的裁决是有效的,但比投票或辩论更昂贵.

结论:

  • 多LLM合作为系统审查的抽象选效率提供了实质性的改进,由模型多样性驱动.
  • 多数投票提供了高性能和低成本的最佳平衡,使其成为领先的策略.
  • 多个代理商的辩论仍然是一个具有成本效益的选择,也是未来技术辅助审查研究的一个有希望的领域.