用说服技巧增强大模型，提升假新闻识别能力
PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation
- 在链式思维中融入说服谬误知识，指导模型推理
- 跨五种模型五类数据集，平均准确率提升15%
- 适合关注虚假信息检测与AI可解释性的研究者
虚假信息检测是媒体素养的关键环节。心理学研究显示，了解说服谬误有助于识别虚假信息。受此启发，我们实验了大型语言模型（LLMs），验证融入说服知识是否能提升虚假信息检测能力。为此，提出新型方法Persuasion-Augmented Chain of Thought（PCoT），通过引入说服机制增强零样本分类中的虚假信息检测。我们在在线新闻和社交媒体帖子上对PCoT进行了广泛评估，并发布两个新构建的、时效性强的虚假信息数据集：EUDisinfo与MultiDis。这些数据集包含在大模型知识截止时间之后发布的文本，确保测试内容对模型完全未见。结果显示，PCoT在五种模型与五类数据集上平均性能超越现有方法15%。该结果表明，利用说服知识能有效强化零样本虚假信息检测能力。
原文摘要 · Abstract (English)
Disinformation detection is a key aspect of media literacy. Psychological studies have shown that knowledge of persuasive fallacies helps individuals detect disinformation. Inspired by these findings, we experimented with large language models (LLMs) to test whether infusing persuasion knowledge enhances disinformation detection. As a result, we introduce the Persuasion-Augmented Chain of Thought (PCoT), a novel approach that leverages persuasion to improve disinformation detection in zero-shot classification. We extensively evaluate PCoT on online news and social media posts. Moreover, we publish two novel, up-to-date disinformation datasets: EUDisinfo and MultiDis. These datasets enable the evaluation of PCoT on content entirely unseen by the LLMs used in our experiments, as the content was published after the models' knowledge cutoffs. We show that, on average, PCoT outperforms competitive methods by 15% across five LLMs and five datasets. These findings highlight the value of persuasion in strengthening zero-shot disinformation detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。