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利用大型语言模型从生物医学文献中提取癌症疫苗辅助剂名称.

Hasin Rehana1,2, Jie Zheng3, Feng-Yu Yeh3

  • 1Department of Biomedical Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND, USA.

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大型语言模型 (LLM) 在识别癌症疫苗辅助剂名称方面表现有前途. GPT-4o和Llama模型实现了显著的召回,在辅助名称提取方面表现优于传统方法.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 疫苗学 疫苗学 疫苗学
  • 人工智能的人工智能

背景情况:

  • 准确识别癌症疫苗辅助剂对于药物开发至关重要.
  • 现有的从文献中提取助剂名称的方法可能是劳动密集型的.
  • 大型语言模型 (LLM) 提供了自动化这一过程的潜力.

研究的目的:

  • 评估各种LLM在识别癌症疫苗辅助剂名称方面的表现.
  • 将LLM的表现与已建立的生物医学文本挖掘模型进行比较.
  • 评估快速工程和少数镜头学习对提取精度的影响.

主要方法:

  • 使用生成预训练变压器 (GPT),拉玛和杰玛模型.
  • 在AdjuvareDB和Vaccine Adjuvant Compendium (VAC) 数据集上采用了零和少数射击学习范式.
  • 设计了特定提示以提取辅助名称和评估上下文影响.

主要成果:

  • 在 AdjuvareDB.o 上,GPT-4o 实现了高性能 (精度:65.9%,回忆:79.7%,F1:69.8%).
  • 在VAC数据集上,Llama-3.2 3B表现出强烈的回忆 (在验证时高达72.5%).
  • 所有测试的LLM都在辅助名称提取任务中表现优于BioBERT.

结论:

  • 一般用途的LLM显示了自动化疫苗辅助剂名称提取的巨大潜力.
  • 通过快速设计和少量学习,可以提高LLM的绩效.
  • 这些发现有助于通过改进数据挖掘来推进疫苗研究.