通过计算文本分析检查嵌入式谎言
Riccardo Loconte1,2, Bennett Kleinberg3,4
1Molecular Mind Lab, IMT School of Advanced Studies Lucca, Lucca, Italy. riccardo.loconte@imtlucca.it.
Scientific reports
|July 21, 2025
概括
检测陈述中嵌入的谎言是具有挑战性的. 一个新的数据集和语言模型表明,真实和欺骗性的陈述部分是常见的,需要先进的口头欺骗检测方法.
科学领域:
- 心理学 心理学 心理学
- 语言学的语言学.
- 计算机科学 计算机科学
背景情况:
- 口头欺骗检测通常假定陈述是完全真实的或欺骗性的.
- 关于嵌入式谎言的研究,在单一陈述中存在真实和欺骗元素,是不发达的.
- 一个连续的真实性,包括嵌入的谎言,提供了一个更现实的看法欺骗.
研究的目的:
- 介绍一个新的数据集真实和欺骗性的陈述与注释嵌入谎言.
- 调查嵌入式谎言的特征及其对欺骗检测的影响.
- 评估语言模型在识别包含嵌入谎言的陈述中的有效性.
主要方法:
- 收集了2,088个真实和欺骗性陈述的数据集,并嵌入了谎言注释.
- 采用主体内部设计,参与者创建自传事件的真实和欺骗版本.
- 利用精心调整的Llama-3-8B语言模型来分类语句,并分析语言特性和个体差异.
主要成果:
- 语言模型在分类真实陈述和嵌入谎言的陈述中获得了64%的准确性,远高于偶然.
- 嵌入式谎言占欺骗性陈述的约三分之一,在语言上往往类似于真实的陈述.
- 个人差异和语言分析表明,真实和欺骗性组件之间的相似性挑战检测.
结论:
- 欺骗性陈述的很大一部分包含嵌入式谎言,需要在口头欺骗检测方面取得进展.
- 开发的数据集为未来研究嵌入式谎言提供了宝贵的资源.
- 未来的研究应该专注于改进检测陈述中的细微欺骗.
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