Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have demonstrated competitive performance with the help of their rich prior knowledge and excellent in-context learning abilities. However, existing methods face significant limitations, such as the Understanding Ambiguity and Information Scarcity, which significantly undermine the potential of LLMs. To address these shortcomings, we propose a Dual-perspective Augmented Fake News Detection (DAFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DAFND first identifies the keywords of each news article through a Detection Module. Subsequently, DAFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to derive its respective two prediction results. Finally, a Determination Module further integrates these two predictions and derives the final result. Extensive experiments on two publicly available datasets show the efficacy of our proposed method, particularly in low-resource settings.

翻译：小样本虚假新闻检测（FS-FND）旨在极低资源场景下区分虚假新闻与真实新闻。由于虚假新闻在社交媒体上的广泛传播及其有害影响，该任务日益受到关注。大语言模型（LLM）凭借其丰富的先验知识和出色的上下文学习能力，已展现出具有竞争力的性能。然而，现有方法面临显著局限，如理解模糊性和信息稀缺性，严重制约了LLM的潜力。为克服这些缺陷，我们提出一种双视角增强型虚假新闻检测（DAFND）模型，旨在从内部与外部双重视角增强LLM。具体而言，DAFND首先通过检测模块识别每篇新闻文章的关键词。随后，DAFND创新性地设计调查模块，以检索与当前新闻相关的内部及外部有价值信息，继而通过判断模块分别得出两项预测结果。最终，判定模块进一步整合这两项预测并推导出最终结果。在两个公开数据集上的大量实验验证了所提方法的有效性，尤其在低资源设置下表现突出。