用大型语言模型进行事实核查的危险和希望
Dorian Quelle1,2, Alexandre Bovet1,2
1Department of Mathematical Modeling and Machine Learning, University of Zurich, Zurich, Switzerland.
Frontiers in artificial intelligence
|February 22, 2024
概括
大型语言模型 (LLM) 显示了通过使用上下文数据验证索赔的自动事实检查的前景. 然而,它们的准确性各不相同,需要进一步研究它们在打击错误信息方面的能力和局限性.
科学领域:
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
- 计算语言学 计算语言学
背景情况:
- 错误信息的扩散需要超出人类能力的自动化事实核查解决方案.
- 大型语言模型 (LLM) 越来越多地用于内容生成和信息验证.
- 了解事实核查中的LLM能力对于维护信息完整性至关重要.
研究的目的:
- 评估LLM代理人在自动化事实核查任务中的有效性.
- 评估上下文信息对LLM事实检查性能的影响.
- 在索赔验证中确定影响LLM准确性的因素.
主要方法:
- LLM 代理被设计为语句查询,检索上下文数据,并做出验证决策.
- 实施了一个框架,其中代理人解释了他们的理由并引用了来源.
- 使用不同的LLM版本 (GPT-4与GPT-3) 和不同的索赔真实性和查询语言来评估性能.
主要成果:
- 当提供上下文信息时,LLM代理人表现出更强的事实核查能力.
- 在准确度方面,GPT-4的性能一般优于GPT-3.
- 发现事实检查的准确性取决于查询语言和声明的真实性.
结论:
- 作为自动化事实核查工具,LLM显示出巨大的潜力.
- 不一致的准确性凸显了需要谨慎和进一步调查的必要性.
- 未来的研究应该专注于了解LLM代理人在事实核查中取得成功或失败的具体条件.
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