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使用大型语言模型进行几次生物医学命名实体识别的提醒

Yao Ge, Sudeshna Das, Yuting Guo

Research square

|September 5, 2025

概括

带有提取增强生成的动态提示显著提高了大型语言模型 (LLM) 中的少数镜头生物医学命名实体识别 (NER) 性能. 这种适应性策略提高了有限数据的准确性,改善了NER任务的结果.

科学领域:

自然语言处理 (NLP)
生物医学信息学

背景情况:

生物医学命名实体识别 (NER) 对于从生物医学文本中提取信息至关重要.
大型语言模型 (LLM) 显示了NER的潜力,特别是在数据有限的少数场景中.

研究的目的:

提高生物医学NER的LLM性能.
研究动态提示策略的有效性,特别是提取增强生成 (RAG).

主要方法:

实施并比较LLM的静态和动态提示工程技术.
使用提取增强生成 (RAG) 与TF-IDF和SBERT进行动态样本选择.
在5次和10次射击设置中评估了5个生物医学NER数据集的性能.

主要成果:

通过结构化组件进行静态提示,GPT-4,GPT-3.5和LLaMA 3-70B的F1分数提高了11-12%.
动态提示进一步提高了性能,TF-IDF和SBERT检索的平均F1得分分别提高了7.3%和5.6%.
动态RAG在一些生物医学NER任务中表现出卓越的结果.

结论:

通过RAG进行情境适应提示对于改善少量生物医学NER非常有效.
动态提示策略在基于LLM的生物医学NLP任务中比静态方法具有显著的进步.

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