HiPrompt:通过层次导向的提示来实现少量生物医学知识融合
Jiaying Lu1, Jiaming Shen2, Bo Xiong3
1Emory University, USA.
概括
这项研究介绍了HiPrompt,这是一种用于生物医学知识融合的新框架. HiPrompt利用大型语言模型和以层次为导向的提示来改善语义理解和医疗决策中的数据集成.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 知识表示 知识表示
背景情况:
- 全面的生物医学知识库对于加强医疗决策至关重要.
- 知识融合需要使用统一的索引系统整合多样化的知识图表,通常具有层次组织以获得细粒度.
- 生物医学知识融合 (BKF) 的现有无监督方法由于依赖于词汇和结构匹配而缺乏语义丰富性.
研究的目的:
- 解决生物医学知识融合 (BKF) 缺乏监督的挑战.
- 为了弥合稀缺标记的BKF数据和神经嵌入模型的数据要求之间的差距.
- 提出一个有效的监督框架,利用大型语言模型的短暂推理能力.
主要方法:
- 开发了HiPrompt,这是一个新的知识融合框架.
- 从事以层次为导向的提示,从大型语言模型中引出一些射击推理.
- 采用了有效的监督方法来克服BKF的数据稀缺性.
主要成果:
- 在KG-Hi-BKF基准数据集上证明了HiPrompt的有效性.
- 展示了HiPrompt捕捉生物医学实体和术语丰富语义的能力.
- 以有效的监督方式验证了框架的表现.
结论:
- HiPrompt为生物医学知识融合提供了一个有效的解决方案.
- 该框架通过利用语义理解和层次上下文,成功地整合了知识图.
- 在将大型语言模型应用于复杂的生物医学信息学任务方面,HiPrompt代表了重大进步.
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