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使用大型语言模型进行口头谎言检测.

Riccardo Loconte1, Roberto Russo2, Pasquale Capuozzo3

  • 1Molecular Mind Lab, IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 55100, Lucca, LU, Italy. riccardo.loconte@imtlucca.it.

Scientific reports
|December 22, 2023
PubMed
概括
此摘要是机器生成的。

像FLAN-T5这样的大型语言模型在自动谎言检测方面表现有希望,在口头欺骗分类任务中取得了最先进的结果. 较大的模型表现更好,与认知负载相关的语言特征影响了预测.

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科学领域:

  • 自然语言处理自然语言处理.
  • 计算语言学 计算语言学
  • 人工智能的人工智能

背景情况:

  • 人类的谎言检测准确性有限,往往不超过机会水平.
  • 使用机器学习和变压器模型的自动口头谎言检测方法已经开发出来,以提高准确性.
  • 大型语言模型 (LLM) 是一种新的方法来增强欺骗检测能力.

研究的目的:

  • 评估FLAN-T5,一个大型语言模型的性能,在不同英语数据集中对欺骗进行分类.
  • 为了比较小型和基本FLAN-T5模型尺寸对谎言检测的有效性.
  • 调查数据集组成和模型大小对欺骗检测准确性的影响.

主要方法:

  • 进行了造型测量分析,以确定跨数据集的语言差异 (个人意见,自传记忆,未来的意图).
  • FLAN-T5 (小型和基本尺寸) 在三个交叉验证场景中进行了测试,在训练和测试集之间进行了不同的数据分布.
  • 用10倍交叉验证来评估性能,将结果与现有基准进行比较.

主要成果:

  • 在情景1和3中,FLAN-T5实现了最先进的性能,超过了以前的基准.
  • 模型性能与模型大小有正相关;较大的FLAN-T5模型显示出更高的准确性.
  • 造型测量分析表明,与认知负载框架相关的语言特征可能会显著影响模型预测.

结论:

  • FLAN-T5显示出作为口头谎言检测的自动化工具的巨大潜力.
  • 模型大小是实现欺骗检测任务最佳性能的关键因素.
  • 了解语言特征,特别是与认知负载相关的特征,可以提高基于LLM的谎言检测系统的可解释性和有效性.