从使用大型语言模型的文献中开发生物医学知识库的基础 - - 一个系统的评估

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Chen Miao¹, Zhenghao Zhang², Jiamin Chen²

¹School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.

Computational and structural biotechnology journal

|August 8, 2025

概括

评估大型语言模型 (LLM) 对于生物医学知识提取至关重要. 我们的基准显示了显著的性能差异,突出了需要仔细及时的工程和源验证的需要.

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:

大型语言模型 (LLM) 显示了生物医学应用的潜力.
从生物医学文献中提取可靠的知识,对于知识基础的发展至关重要.
目前在这个领域评估LLM可靠性的方法是不够的.

研究的目的:

开发和利用一个基准来比较生物医学知识提取中的LLM绩效.
在11个不同的知识提取任务中评估LLM能力.
调查特定任务示例对LLM绩效的影响.

主要方法:

为11个文学知识提取任务创建一个基准套件.
对这些任务进行多个LLM的比较,有或没有上下文示例.
基于任务复杂性,数据特征和输出要求,对LLM绩效的分析.

主要成果:

在不同的LLM和任务中观察到显著的性能变化.
士学位的表现受技术专业化,任务难度,信息分散和标准化需求的影响.
要求LLM引用源文本可以提高可靠性,但也带来了即时的工程挑战.

结论:

生物医学知识提取的LLM绩效是高度可变和任务依赖的.
谨慎的提示设计,包括引用源文本,对于可靠的LLM应用程序是必要的.
需要进一步的研究来优化LLM提示,以获得强大的生物医学知识提取.

关键词:

生物医学知识库生物医学知识库大型语言模型.快递工程是指快递的工程.

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