在临床自然语言处理中利用大型语言模型进行无知识的弱监督
1University of Texas Health Science Center at Houston.
Research square
|July 9, 2024
概括
这项研究引入了一种用于临床自然语言处理 (NLP) 的新方法,使用大型语言模型 (LLM) 来生成弱标记的数据,以最小的黄金标准数据显著提高模型性能.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的机器学习
- 临床信息学 临床信息学
背景情况:
- 临床自然语言处理 (NLP) 在很大程度上依赖于大型标记数据集,这些数据集在医疗领域稀缺且昂贵.
- 使用大型语言模型 (LLM) 进行弱监督和上下文学习是有前途的,但监督方法的表现往往不佳.
- 士学位推断在计算上是密集的,这对广泛的临床采用构成了障碍.
研究的目的:
- 开发一个高效和有效的NLP方法用于临床领域使用LLMs和弱监督.
- 减少对广泛的黄金标准临床数据的依赖,以训练高性能模型.
- 研究一种方法,以最小的领域专业知识和标记数据实现主导性能的方法.
主要方法:
- 一种基于提示的方法,使用微调的LLM (Llama2-13B) 来生成标记较弱的数据.
- 使用LLM生成的弱标记数据训练下游BERT模型.
- 在少量黄金标准临床数据的基础上,进一步微调弱监管的BERT模型.
- 在三个n2c2数据集上进行评估,以根据已建立的基准来评估性能.
主要成果:
- 拟议的方法,使用Llama2-13B进行弱监督,在F1得分中以4.7-47.9%的比率始终优于现成的PubMedBERT,只有10张黄金标准纸币.
- 只有50张黄金标准纸币,开发的模型实现了与完全监督系统相提并论的性能.
- 该方法在不同的临床数据集中显示出强大的性能.
结论:
- 微调弱监督的LLM为临床NLP数据稀缺提供了强大的解决方案.
- 这种方法显著提高了下游模型的性能,如BERT,使用最小的标记数据.
- 该方法为临床文本分析提供了计算效率高和高效的策略,减少了大量手动注释的需求.
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