超越微调:利用域识别语境学习,使用大型语言模型进行临床命名实体识别
Siun Kim1, David Seung U Lee2, Yujin Kim2
1Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea.
Journal of biomedical informatics
|January 10, 2026
概括
用大型语言模型 (LLM) 优化上下文学习 (ICL) 与临床命名实体识别 (NER) 的编码器微调匹配或超过. 这种方法可以在医疗机构提供高效的适应和持续改进,而不需要再培训.
科学领域:
- 自然语言处理自然语言处理.
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 临床命名实体识别 (NER) 对于组织非结构化临床文本至关重要.
- 虽然大型语言模型 (LLM) 通过上下文学习 (ICL) 提供无参数的适应,但基于编码器的微调传统上导致了临床NER的性能.
- 本研究在现实条件下系统地比较ICL和微调,以评估其有效性.
研究的目的:
- 进行ICL和基于编码器的微调对临床NER进行系统的比较.
- 评估优化ICL演示选择对性能的影响.
- 为了确定ICL在资源有限的临床环境中的可行性.
主要方法:
- 利用了来自血液恶性瘤患者的2,113份注释临床笔记和400份MIMIC-IV笔记.
- 优化了ICL配置,包括使用LLaMA-3.3-70B的指令,输出格式和演示选择策略.
- 使用RoBERTa-large作为基线进行编码器微调,评估各种场景 (域内,跨域,跨机构) 的代币级分类的所有模型.
主要成果:
- 优化的示范选择显著改善了ICL性能,将宏观F1提高了高达9.4个点.
- 在温和的设置中,ICL的性能优于RoBERTa的大微调,并且在较大的样本池中保持竞争力.
- ICL展示了卓越的数据效率,在没有参数更新的情况下实现了跨机构传输的实质性收益,尽管微调导致了最大的池大小.
结论:
- 优化的域意识示范选择允许基于开源LLM的ICL与临床NER的编码器微调相等或超过.
- 通过示范池,不需要再培训,ICL的适应性和知识更新能力有利于动态的,资源有限的医疗环境.
- 这有助于在医疗保健领域不断改进临床NLP任务.
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