使用大型语言模型进行可扩展的科学兴趣分析
Yilun Liang1, Gongbo Zhang2, Edward Sun3
1Tandon School of Engineering, New York University, Brooklyn, NY, USA.
Journal of biomedical informatics
|November 2, 2025
概括
大型语言模型 (LLM) 可以自动化科学兴趣分析. 使用医学主题标题 (MeSH) 术语生成的个人资料更易于阅读,尽管人类编写的个人资料提供了更多的新概念.
科学领域:
- 生物医学信息学是生物医学信息学.
- 在研究中的人工智能.
背景情况:
- 科学研究资料对于人才发现和合作至关重要.
- 现有的配置文件往往过时,需要自动化和可扩展的解决方案.
- 大型语言模型 (LLM) 为动态配置文件生成提供了一个潜在的解决方案.
研究的目的:
- 设计和评估基于LLM的方法来生成科学兴趣的个人资料.
- 将机器生成的个人资料与研究人员自我总结的兴趣进行比较.
- 评估从PubMed摘要与医学主题标题 (MeSH) 术语中生成的个人资料的性能.
主要方法:
- 开发了两种基于LLM的方法:一种是总结研究人员的摘要,另一种是使用MeSH术语.
- GPT-4o-mini被用来生成来自哥伦比亚大学欧文医疗中心的595名研究人员的摘要.
- 为了进行比较,使用了自动化指标 (ROUGE-L,BLEU,METEOR,BERTScore,KL Divergence) 和手动评估.
主要成果:
- 自动化指标显示,机器生成的和人类编写的个人资料之间的词汇重叠很小,但语义相似性中等 (BERTScore F1:~0.55).
- 手动转述的摘要获得了更高的相似性 (F1: 0.851).
- 基于MeSH的个人资料显示出优越的可读性 (93.44%的好评),并且在67.86%的手册评论中更喜欢,尽管与人写的个人资料相比,关键词使用和事实准确性存在差异.
结论:
- 在科学兴趣分析的可扩展自动化方面,LLM显得有前途.
- 基于MeSH的LLM生成的配置文件提供了比基于抽象的更好的可读性.
- 虽然LLM可以生成语义上类似的配置文件,但人类撰写的摘要往往会引入更多的新概念.
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