在系统性审查中开发用于大型语言模型驱动的选的快速模板
Christian Cao1, Jason Sang2, Rohit Arora3
1Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, and Centre for Health Informatics, Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada (C.C.).
Annals of internal medicine
|February 24, 2025
概括
大型语言模型 (LLM) 显著提高了系统审查 (SR) 选效率. 优化的LLM提示实现了抽象和全文选的高准确性,降低了成本和时间.
科学领域:
- 医疗信息学医学信息学
- 在研究中的人工智能.
- 证据综合的方法论.
背景情况:
- 系统审查 (SRs) 由于严格的初始文章选而面临延误.
- 有效的信息综合对于及时获得可靠证据至关重要.
研究的目的:
- 开发可适应的,通用的提示模板,用于以大型语言模型 (LLM) 驱动的SRs中的抽象和全文选.
- 提高SR选过程的效率,降低SR选过程的成本.
主要方法:
- 使用GPT-4开发并测试了通用提示模板,用于在10个SR中进行抽象和全文选.
- 将LLM选性能 (敏感性,特异性) 与原始SR作者决定进行比较.
- 通过将LLM选时间和成本与手动选进行比较,评估了成本效益.
主要成果:
- 优化的LLM提示实现了高加权的敏感性 (97.7%的抽象,96.5%的全文) 和具体性 (85.2%的抽象,91.2%的全文).
- 零射击提示显示灵敏度较低 (49.0%的抽象,49.1%的全文).
- 基于LLM的10,000个引用的选成本为157.02美元,而手动选的估计成本为1666.67美元.
结论:
- 一般的,优化的LLM提示可以适应SRs中高效的抽象和全文选.
- 这种方法为进行证据综合的研究人员节省了大量的时间和成本.
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