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通过对大型语言模型的指令调整,在生物医学中推进实体识别

Vipina K Keloth¹, Yan Hu², Qianqian Xie¹

¹Section of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT-06510, United States.

Bioinformatics (Oxford, England)

|March 21, 2024

概括

我们开发了一种新方法,使用大型语言模型 (LLM) 改进生物医学命名实体识别 (NER). 我们的方法将NER转化为生成任务,超越现有模型并展示LLMs.

科学领域:

计算语言学计算语言学
生物信息学是一种生物信息学.
医疗保健中的人工智能

背景情况:

大型语言模型 (LLM) 在医疗保健方面表现有前途,但在生物医学命名实体识别 (NER) 上扎.
生物医学NER通常是一个序列标记任务,这种格式对于LLMs来说不是最佳的.
与生物医学NER专业化,微调模型相比,现有的LLM表现不佳.

研究的目的:

开发一个基于指令的学习范式,以适应生物医学NER的LLMs.
将生物医学NER任务从序列标记转变为生成任务.
评估LLM在生物医学NER任务上的表现,使用重用数据集.

主要方法:

为生物医学NER开发了一个端到端的基于指令的学习范式.
重新利用现有的生物医学NER数据集进行培训和评估.
使用LLaMA-7B作为基础的LLM,创建了BioNER-LLaMA.

主要成果:

在各种生物医学NER数据集上,BioNER-LLaMA获得了比GPT-4更高的F1分数 (530%).
证明通用领域的LLM可以匹配微调的,域特定的模型,如PubMedBERT.
与生物医学特定的PMC-LLaMA模型相比,展示了竞争性表现.

结论:

拟议的范式有效地将通用领域的LLM适应于高性能生物医学NER.
这种方法使LLM能够在多任务,多领域的生物医学应用中与最先进的性能竞争.
该研究强调了生成LLM在复杂的生物医学NLP任务中的潜力.

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