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大型语言模型显示多语言事实检查中的宁-克鲁格效应
Ihsan Ayyub Qazi1, Zohaib Khan2, Abdullah Ghani2
1Department of Computer Science, Lahore University of Management Sciences, Lahore, 54792, Pakistan. ihsan.qazi@lums.edu.pk.
Scientific reports
|February 25, 2026
概括
大型语言模型 (LLM) 在全球范围内显示出不同的事实检查准确性. 较小的模型过于自信,冒着偏见的风险,特别是对于非英语索赔,突出需要公平的AI解决方案.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 信息科学 信息科学 信息科学
背景情况:
- 错误信息的扩散需要可扩展和可靠的事实核查技术.
- 大型语言模型 (LLM) 提供了自动事实验证的潜力,但它们的全球有效性尚未被证明.
研究的目的:
- 系统地评估9个不同的LLM在5000个事实检查索赔的多语言数据集上的表现.
- 评估LLM对培训后数据的概括性以及不同提示策略的影响.
主要方法:
- 评估了9个LLM的各种类别 (开源/封闭源,大小,架构,推理).
- 利用了174个组织在47种语言中对5000个索赔进行了事实检查,有超过24万条人类注释.
- 测试了对索赔的模型性能,培训截止时间和四种不同的提示策略.
主要成果:
- 观察到Dunning-Kruger效应:较小的模型表现出高可信度但低准确度,而较大的模型表现出更高的准确度与较低的信心.
- 对于非英语语言和来自全球南方的索赔出现了显著的绩效差异,这可能会加剧信息不平等.
- 模型通用性和提示策略影响了准确性,在LLM类型和语言之间存在显著差异.
结论:
- 当前的法学士在事实核查方面的表现面临系统偏见的风险,特别是不利于资源有限的组织和扩大全球信息差距.
- 建立了LLM事实检查的多语言基准,强调需要公平的AI开发和部署.
- 调查结果为政策干预提供了证据基础,以确保公平获得可靠的AI辅助事实核查.
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