LLM-增强虚假新闻的多式联络检测
Jingwei Wang1, Ziyue Zhu2, Chunxiao Liu3
1School of Humanities and Communication, Zhejiang Gongshang University, Hangzhou, China.
PloS one
|October 24, 2024
概括
本研究介绍了FND-LLM框架,将小型和大型语言模型 (LLM) 结合起来,用于先进的多式联络虚假新闻检测. 该框架通过整合文本,视觉和改分析来提高准确性,优于现有的方法.
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:
- 在数字时代,检测假新闻至关重要.
- 小语言模型 (SLMs) 难以获得专业知识,限制了虚假新闻的检测.
- 现有的方法缺乏强大的多式联运分析能力.
研究的目的:
- 提出FND-LLM框架,用于增强多式联运虚假新闻检测.
- 利用SLM和大型语言模型 (LLM) 的互补优势.
- 探索LLMs在分析多式联络新闻内容方面的潜力.
主要方法:
- FND-LLM框架整合了文本,视觉语义和视觉改特征分支.
- 一个共同注意网络精细化了文本和视觉信息之间的相互关系.
- 使用CLIP和LLM推断进行辅助解释.
主要成果:
- FND-LLM框架在现有模型中表现出优越的性能.
- 在微博上实现了0.7%的精度改进,在gossipcop上6.8%,在politifact.com上1.3%.
- 有效地集成多式联运数据,以更强大的假新闻检测.
结论:
- FND-LLM框架在多式联运假新闻检测方面取得了重大进展.
- 结合SLM和LLM可以提高检测准确性,并解决以前方法的局限性.
- 该框架为未来对抗错误信息的研究提供了一个有希望的方向.
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