协作大型语言模型 (LLM) 是您在系统性审查中选所需的一切
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
协作大型语言模型 (LLM) 显著提高了系统审查选的效率和性能. 这种方法节省了大量的人力资源,支持持续更新证据.
科学领域:
- 医疗研究中的人工智能
- 自然语言处理用于证据合成.
背景情况:
- 系统性审查 (SRs) 需要严格的研究选,这一过程往往因人工努力而陷入瓶.
- 大型语言模型 (LLM) 实现SR查自动化和增强的潜力在很大程度上尚未被探索.
研究的目的:
- 评估LLMs在自动化系统审查选过程中的有效性.
- 为了比较单个LLM与协作LLM方法的性能.
主要方法:
- 一项使用标记数据 (标题和摘要) 来自五个SR的观察性研究.
- 使用了个人LLM (GPT-4,克劳德-3-索内特,双子-Pro-1.0) 和协作LLM策略.
- 绩效指标包括准确性,排除精度,包含回忆和在样本 (WSS) 上节省的工作.
主要成果:
- 个别的LLM在排除 (高达99.7%) 和回忆 (高达96.6%) 方面表现出很高的精度.
- 协作LLM方法,特别是使用GPT-4和Claude-3S,实现了更高的平均精度 (99.9%) 和回忆 (98.5%).
- 协作LLM的平均WSS为63.5%,明显高于单个模型 (45.2%).
结论:
- 协作LLM为系统审查选提供了一种高效和高性能解决方案.
- 这种自动化支持证据综合的及时和持续更新.
- 未来的研究应该在各种数据集和专有模型中探索LLM的能力.
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