通过LLMs的外部知识蒸来加强以文本为中心的假新闻检测
Xueqin Chen1, Xiaoyu Huang2, Qiang Gao3
1Kash Institute of Electronics and Information Industry, Kashgar, China.
概括
本研究介绍了LEKD,这是一种新的虚假新闻检测方法,它集成了小语言模型 (SLM),外部知识和大语言模型 (LLM). 通过结合模型的优势和降低计算成本,LEKD提高了假新闻检测的准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
背景情况:
- 检测假新闻对于社会福祉至关重要,文本内容是主要指标.
- 现有的方法,如基于SLM,外部知识增强和基于LLM的方法,在概括,计算成本和事实推理方面存在局限性.
研究的目的:
- 提出LEKD,一种混合方法,可以克服当前假新闻检测方法的局限性.
- 通过协同利用SLM,外部知识和LLMs来提高以文本为中心的假新闻检测的准确性和效率.
主要方法:
- LEKD利用LLM来产生用于训练数据增强的外部知识.
- 一个基于图形的语义感知特征对齐模块解决了知识矛盾.
- 一个基于信息瓶的知识蒸模块确保在推断过程中隐含的特征生成.
主要成果:
- 与基线方法相比,LEKD在假新闻检测方面表现优越.
- 提出的方法有效地结合了SLM,外部知识和LLMs的优势.
- 实验结果在两个基准数据集上验证了LEKD的优势.
结论:
- 对于以文本为中心的假新闻检测,LEKD提供了一个强大而高效的解决方案.
- 在LEKD中整合各种AI技术显著提高了检测能力.
- 这种方法解决了当前假新闻检测范式的关键挑战.
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