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用检索增强的大型语言模型进行COVID-19事实检查:开发和可用性研究

Hai Li¹, Jingyi Huang¹, Mengmeng Ji²

¹School of Economics and Management, Shanghai University of Sport, Shanghai, China.

Journal of medical Internet research

|April 30, 2025

概括

采用大型语言模型 (LLM) 的检索增强生成 (RAG) 显著提高了COVID-19事实检查的准确性. 像CRAG和SRAG这样的先进RAG模型大大减少了错误信息和幻觉,以便可靠地验证信息.

科学领域:

人工智能的人工智能
公共卫生信息学公共卫生信息学
计算语言学计算语言学

背景情况:

随着COVID-19的流行,错误信息的"传染病"蔓延,压倒了传统的事实核查.
大型语言模型 (LLM) 提供可扩展的解决方案,但容易产生幻觉.
在LLM可靠性的局限性阻碍有效,大规模的错误信息打击.

研究的目的:

为了提高COVID-19事实核查的准确性和可靠性.
为了解决LLM幻觉和上下文不准确性,使用检索增强生成 (RAG).
为了评估RAG增强的LLM绩效与错误信息对抗.

主要方法:

开发了RAG增强模型 (原始RAG,LOTR-RAG,CRAG,SRAG) 与GPT-4集成.
利用了~13万篇COVID-19同行评审论文的数据集,以获得上下文.
在真实世界和合成数据集 (每个500个索赔) 上评估模型的准确性,F1分数,精度和灵敏度.

主要成果:

在两个数据集上,RAG模型显著提高了比基线GPT-4的准确性.
CRAG和SRAG模型实现了最高的精度 (0.972和0.973在现实世界;0.978在合成).
RAG 持续减少了幻觉和提高了语境准确性,特别是 CRAG 和 SRAG.

结论:

将RAG系统与LLM集成,大大提高了自动事实检查的准确性和相关性.
这种方法提供了快速,可靠的信息验证,以打击公共卫生错误信息.
通过引用来源,RAG提高了透明度,这对于健康危机期间的信任至关重要.

关键词:

在 COVID-19 疫情中,聊天GPT 聊天在GPT 聊天这就是SARS-CoV-2病毒.准确度准确度准确度准确度准确度人工智能的人工智能是人工智能.冠状病毒冠状病毒病毒虚假信息虚假信息是一种错误的信息.检查事实 - 检查事实传染性传染性传染性传染病感染的流行病.大型语言模型机器学习是机器学习.错误的信息错误的信息自然语言处理自然语言处理.流行病是一种流行病.肺动脉肺动脉是什么意思呼吸道呼吸道呼吸道提取增强生成的提取

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