使用大型语言模型从患者衍生癌症模型的科学文本中提取知识:算法开发和验证研究研究
Jiarui Yao1,2, Zinaida Perova3, Tushar Mandloi3
1Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Boston, MA, United States, 1 7813545014.
JMIR bioinformatics and biotechnology
|December 4, 2025
概括
软提示显著提高了开放式大语言模型 (LLM) 的性能,用于从科学文本中提取患者衍生癌症模型 (PDCM) 实体,与专有模型竞争.
科学领域:
- 生物医学信息学 生物医学信息学
- 在瘤学中使用人工智能
- 计算生物学 计算生物学
背景情况:
- 患者衍生癌症模型 (PDCMs) 对于癌症研究和临床前研究至关重要.
- 与PDCM相关的出版物数量急剧增加,需要有效的知识提取.
- 大型语言模型 (LLM) 提供了在规模上处理科学文献的高级功能.
研究的目的:
- 研究基于LLM的系统,用于自动提取与PDCM相关的实体.
- 为了比较实体提取的直接提示和软提示技术.
主要方法:
- 探索了直接提示 (手动提示设计) 和软提示 (可训练的连续向量).
- 在专有 (GPT4-o) 和开放 (LLaMA3) LLMs中评估了这两种方法.
- 使用了100个PDCM摘要的手动注释数据集,其中包含15个实体类型.
主要成果:
- 通过直接提示的GPT4-o实现了F1得分50.48 (精确匹配) 和71.36 (重叠匹配).
- 与直接提示相比,LLaMA3软提示显著提高了性能 (准确匹配:7.06至46.68;重叠匹配:12.0至71.80).
- 在重叠匹配设置中,LLaMA3软提示略高于GPT4-o直接提示.
结论:
- 软提示可以提高较小的开放LLM的性能,用于PDCM实体提取.
- 在开放模型上训练软提示可以产生与专有LLM相比的性能.
- 这种方法在PDCM研究中促进了可扩展的知识发现.
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