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有限数据的高效医疗NER:通过注释指南提高LLM绩效

Emiko Shinohara1, Yoshimasa Kawazoe1

  • 1Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

International journal of medical informatics
|December 23, 2025
PubMed
概括

将详细的注释指导方针纳入提示符可以显著改善医疗命名实体识别 (NER) 用大型语言模型 (LLM) 的几次射击学习. 这种方法提高了回忆和F1分数,为资源有限的NLP开发提供了实际解决方案.

科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 人工智能 (AI) 是一种人工智能.
  • 计算语言学 计算语言学

背景情况:

  • 命名实体识别 (NER) 在医学NLP中对于识别关键临床信息至关重要.
  • 传统的NER方法需要大量的注释数据,这带来了资源挑战.
  • 大型语言模型 (LLM) 为NER提供了创新的短暂学习方法.

研究的目的:

  • 评估LLM中注释指南对少数NER性能的影响.
  • 为了评估这种影响在不同的医学文本体.

主要方法:

  • 设计了八个提示模式,结合了几次拍摄的例子,并具有不同复杂度的注释指南.
  • 在三个医疗体 (i2b2-2014,i2b2-2012,MedTxt-CR) 上,使用三个LLM (GPT-4o,Claude 3.5 Sonnet,gpt-oss-120b) 评估了绩效.
  • 精度指标包括精度,回忆和F1得分,与相关的共享任务保持一致.

主要成果:

  • 将详细的注释指南纳入短暂提示中,通常会导致回忆和F1分数的改善.
  • 特定的提示结构和指导方针的复杂性影响了LLM和企业之间的绩效差异.

结论:

关键词:
人工智能的人工智能是人工智能.大型语言模型.医疗信息学医学信息学命名实体认可 命名实体认可自然语言处理自然语言处理.

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  • 将注释准则集成到LLM提示中是一种有效的策略,可以提高NER的性能,特别是在少数情况下.
  • 该方法为开发准确的医学NLP系统提供了实用和有效的方法,特别是在注释数据有限的环境中.
  • 注释准则对于评估和提示工程都至关重要,优化了医学NLP等专业领域的LLM能力.