一种人类-LLM协作注释方法用于选精密瘤学随机对照试验的文章
Hui Chen1, Jiale Zhao1, Sheng Zheng1
1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310058, China.
BMC medical research methodology
|September 30, 2025
概括
本研究引入了一个人大语言模型 (LLM) 协作注释方法,以提高系统审查的文章选效率. 该方法显著减少工作量,同时保持高可靠性,帮助研究人员在证据合成.
科学领域:
- 图书统计学 图书统计学
- 研究中的人工智能.
背景情况:
- 监督学习加快了系统审查选,但需要大量的手动注释.
- 大型语言模型 (LLM) 提供快速选,但面临可靠性挑战.
- 开发高效可靠的注释方法对于系统审查至关重要.
研究的目的:
- 为文章选设计一种高效可靠的人类-LLM协作注释方法.
- 为了优化LLM的性能,在识别相关文章时进行高回忆.
- 验证协作注释方法的效率和可靠性.
主要方法:
- 开发了一个人类-LLM协作注释策略,重点是验证LLM识别的阳性样本.
- 提示被代优化,使用手动注释的数据集来实现近乎完美的LLM回忆.
- 优化的LLM选文章,然后对积极注释进行人类验证,应用于精密瘤学RCT选.
主要成果:
- 代提示优化实现了近乎完美的LLM回忆 (100%调整,85.71%验证).
- 协作方法实现了F1得分为0.9583,并将注释工作量减少了80%左右.
- 在协作数据上训练的BioBERT模型表现优于仅在LLM数据上训练的模型,证实了可靠性.
结论:
- 人类-LLM协作注释方法提高了文章选的效率和可靠性.
- 这种方法为进行系统审查和元分析提供了重要的支持.
- 这些发现突显了人工智能辅助方法在科学文献评估中的潜力.
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