通过使用带有情感特征的集成LLM来检测欺骗模型
Chucheng Zhou1, Yingqian Zhang2,3, Chengcong Lin4
1School of Computing and Data Science, Xiamen University Malaysia, Sepang, Selangor, 43900, Malaysia.
Scientific reports
|September 1, 2025
概括
这项研究引入了情感增强的欺骗检测模型,使用基于RoBERTa的情感特征 (LieXBerta) 的XGBoost进行谎言检测,提高了法庭设置的准确性. 李克伯塔模型的准确性达到87.50%,超过了传统方法.
科学领域:
- 人工智能
- 机器学习
- 自然语言处理
背景情况:
- 传统的谎言检测方法很大程度上依赖于人类审讯者,
- 开发客观准确的欺骗检测系统对于法律和法医应用至关重要.
研究的目的:
- 提出和评估一种情感增强的欺骗检测模型,使用基于RoBERTa的情感特征 (LieXBerta) 的XGBoost进行谎言检测,以提高客观性和准确性.
- 整合情绪,面部和动作特征以更强大的欺骗检测.
主要方法:
- 使用强大优化的BERT预训练方法 (RoBERTa) 来从审讯文本中提取情感特征.
- 结合抽取的情感特征与面部和行动特征.
- 使用极端梯度提升 (XGBoost) 分类器进行欺骗检测任务.
- 开发了一个试验文本数据集,以详细的情感特征进行模型验证.
主要成果:
- 与仅使用传统特征和其他经典机器学习模型的基线模型相比,包含情感特征的 LieXBerta 模型表现出更高的性能.
- 对 LieXBerta 模型的参数调整导致准确率为 87.50%,比基线提高了 6. 5%.
- 调整后的LieXBerta模型的功能减少了42%的运行时间,提高了训练效率和预测性能.
结论:
- 拟议的LieXBerta模型通过整合情感特征显著提高了欺骗检测的客观性和准确性.
- 该模型在法庭环境中显示出有前途的应用潜力,为传统方法提供更可靠的替代方案.
- 整合用于情感提取的RoBERTa和用于分类的XGBoost为高级欺骗检测提供了有效的框架.
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