一种自我学习的多式联络方法,用于检测假新闻
1School of Computer Science, Chengdu University of Information Technology, Chengdu, China.
Frontiers in artificial intelligence
|November 24, 2025
概括
由于数据有限,检测假新闻具有挑战性. 本研究介绍了一种使用对比学习和大型语言模型 (LLM) 进行有效虚假新闻分类的自学多式模式.
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.
- 计算机视觉 计算机视觉
背景情况:
- 社交媒体推动了错误信息的传播,经常结合文字和图像.
- 现有的假新闻检测方法因缺乏标记数据集而困难.
研究的目的:
- 开发一种自我学习的多式联通模式,用于增强假新闻分类.
- 为了应对虚假新闻检测数据稀缺的挑战.
主要方法:
- 利用对比式学习从多式联络数据中进行无监督的特征提取.
- 集成大型语言模型 (LLM) 共同分析文本和图像特征.
- 开发了一种自学方法,用于在没有标记数据的情况下对假新闻进行分类.
主要成果:
- 拟议的多式联运模型实现了超过85%的准确性,精度,回忆和F1分数.
- 在公共数据集上超过了几种最先进的假新闻分类方法.
- 证明了自我学习和LLMs在多式联络假新闻检测中的有效性.
结论:
- 自学多式联络模式通过分析结合的文本和图像数据,有效打击假新闻.
- 相反的学习和LLM提供了一个强大的解决方案,用于虚假新闻检测,尽管数据有限.
- 这种方法显著推进了多式联运虚假信息检测领域.
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