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快速链接:利用大型语言模型进行跨源生物医学概念链接

Yuzhang Xie¹, Jiaying Lu¹, Joyce Ho¹

¹Emory University, USA.

International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval

|February 28, 2025

概括

使用大语言模型 (LLM),PromptLink有效地将生物医学概念与各种数据源联系起来. 这种新的框架通过促使LLM利用他们的知识和对预测进行自我反思来提高准确性.

科学领域:

生物医学信息学生物医学信息学
自然语言处理自然语言处理.
人工智能的人工智能

背景情况:

将跨数据源的生物医学概念联系起来对于整合性分析至关重要,但因命名差异而受到阻碍.
现有的方法 (基于规则的,词汇库,传统的ML) 具有有限的概括性和先前知识.
大型语言模型 (LLM) 提供了丰富的先验知识,但面临着成本,上下文限制和可靠性等挑战.

研究的目的:

引入PromptLink,这是一个生物医学概念链接的新框架,有效地利用LLMs.
通过利用LLM功能来解决现有的概念链接方法的局限性.
开发一种通用且可适应的概念框架,将各种数据类型联系起来.

主要方法:

PromptLink采用生物医学专业预训练模型来生成与LLM兼容的候选概念.
一个两阶段的提示策略是用LLM:首先是引起知识,然后通过自我反思来提高预测可靠性.
该框架的设计是通用的,不需要额外的先前知识,背景或培训数据.

主要成果:

在电子健康记录 (EHR) 数据集和生物医学知识图 (KG) 之间的任务连接概念中,PromptLink 证明了有效性.
这种两阶段提示方法显著提高了基于LLM的概念链接的可靠性.
经验结果验证了框架能够在各种数据源之间进行概念链接的能力.

结论:

PromptLink为生物医学概念链接提供了一种强大而通用的解决方案,克服了以前方法的局限性.
利用新的提示策略来利用LLM可以提高跨数据源概念对齐的准确性和可靠性.
该框架的适应性使其适用于广泛的生物医学数据整合挑战.

关键词:

生物医学概念连接链接几次射击促使促使一些人.对于资源有限的领域的大型语言模型.获取和重新排名获取和重新排名

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