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Updated: May 23, 2025

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大型语言模型作为文献评论工具的出现:大型语言模型辅助的系统审查

Dmitry Scherbakov¹, Nina Hubig^1,2, Vinita Jansari³

¹Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina (MUSC), Charleston, SC 29403, United States.

Journal of the American Medical Informatics Association : JAMIA

|May 7, 2025

概括

大型语言模型 (LLM) 越来越多地用于科学审查自动化. 虽然有效的任务,如搜索和数据提取,LLM辅助的评论显示潜力改变科学研究,尽管目前的局限性.

科学领域:

圣经计量学和科学计量学
研究中的人工智能.
科学出版科学出版

背景情况:

科学审查对于综合研究至关重要,但耗时.
大型语言模型 (LLM) 提供了自动化审查过程的各个阶段的潜力.
了解目前在科学评论中LLM应用的现状至关重要.

研究的目的:

系统地总结LLMs在科学评论创作中的使用情况.
分析方法论文,详细说明LLM在审查自动化中的使用.
检查在他们的方法论中承认LLM支持的审查论文.

主要方法:

在2024年6月,在PubMed,Scopus,Dimensions和谷歌学者中进行了全面的文献搜索.
选和数据提取使用Covidence与LLM附加组件 (OpenAI GPT-4o) 进行.
像ChatGPT和Scite.ai这样的工具协助了数据清理,数字代码生成和手稿起草.

主要成果:

在3788篇获取的文章中,有172篇符合审查条件.
基于GPT的LLM是审查自动化的主要架构 (73.2%).
虽然有许多自动化项目存在,但只有15.1%的论文在实际评论中承认LLM的使用;重点是搜索 (34.9%) 和数据提取 (31.4%).
基于GPT的模型在数据提取方面表现优于基于BERT的模型 (精度:83.0%,回忆:86.0%).

结论:

通过LLM辅助的系统审查凸显了审查自动化日益增长的趋势.
在提高科学审查的效率和范围方面,LLM是有前途的.
尽管存在局限性,特别是在数字数据提取方面,LLM有望对未来的科学审查行为产生重大影响.

关键词:

冠状病毒的共存.大型语言模型.审查自动化审查自动化范围审查范围审查审查系统性审查系统性审查

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