机器学习可以帮助加快系统审查的文章选吗? 是的,当嵌入空间中的文章分离性很高时
Farhan Ali1, Amanda Swee-Ching Tan1, Serena Jun-Wei Wang2
1National Institute of Education, Nanyang Technological University, Singapore, Singapore.
Research synthesis methods
|February 2, 2026
概括
系统审查的自动化策略表现不同. 在机器学习 (ML) 模型中使用集群分离性的新启发式可以预测ML选何时有效地节省工作.
科学领域:
- 图书统计学 图书统计学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 系统审查至关重要,但由于越来越多的研究出版物,需要大量的劳动力.
- 需要自动化方法来加快系统审查流程.
研究的目的:
- 评估各种机器学习 (ML) 模型和大型语言模型 (LLM) 的有效性,以自动化系统审查选.
- 为了确定成功的ML辅助查的预测因素.
主要方法:
- 测试了教育系统审查数据集的经典和深度学习ML模型.
- 通过GPT-3.5和GPT-4LLMs进行评估即时工程,以进行少量学习.
- 作为一个预测器,研究了高维嵌入空间中的集群分离性.
主要成果:
- 在不同数据集中,ML和LLM模型的性能差异很大,工作节省率从1.2%到75.6%不等,回忆率为95%.
- 在不同模型和数据集中,集群分离性强烈预测了ML选实用程序 (R = 0.81).
结论:
- 机器学习选性能是高度可变的.
- 拟议的集群分离度启发式提供了一个可概括的方法来预测和潜在地优化ML辅助选管道.
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