The proliferation of fake news on social media platforms has exerted a substantial influence on society, leading to discernible impacts and deleterious consequences. Conventional deep learning methodologies employing small language models (SLMs) suffer from the necessity for extensive supervised training and the challenge of adapting to rapidly evolving circumstances. Large language models (LLMs), despite their robust zero-shot capabilities, have fallen short in effectively identifying fake news due to a lack of pertinent demonstrations and the dynamic nature of knowledge. In this paper, a novel framework Multi-Round Collaboration Detection (MRCD) is proposed to address these aforementioned limitations. The MRCD framework is capable of enjoying the merits from both LLMs and SLMs by integrating their generalization abilities and specialized functionalities, respectively. Our approach features a two-stage retrieval module that selects relevant and up-to-date demonstrations and knowledge, enhancing in-context learning for better detection of emerging news events. We further design a multi-round learning framework to ensure more reliable detection results. Our framework MRCD achieves SOTA results on two real-world datasets Pheme and Twitter16, with accuracy improvements of 7.4\% and 12.8\% compared to using only SLMs, which effectively addresses the limitations of current models and improves the detection of emergent fake news.

翻译：社交媒体平台上虚假新闻的泛滥已对社会产生显著影响，导致可辨识的冲击与有害后果。采用小语言模型（SLMs）的传统深度学习方法存在两大局限：需要大量监督训练，且难以适应快速演变的现实情境。大语言模型（LLMs）虽具备强大的零样本能力，但由于缺乏相关示例知识及动态知识更新的不足，在有效识别虚假新闻方面仍存在缺陷。本文提出一种新颖框架——多轮协同检测（MRCD），以解决上述问题。MRCD框架能够融合LLMs的泛化能力与SLMs的专用功能，从而兼取二者优势。我们的方法采用两阶段检索模块，选取相关且最新的示例与知识，增强上下文学习能力以更好地检测新兴新闻事件。我们进一步设计多轮学习框架以确保更可靠的检测结果。在Pheme和Twitter16两个真实数据集上，MRCD框架取得了最先进的性能，相较于仅使用SLMs，准确率分别提升7.4%和12.8%，有效克服了现有模型的局限并提升了新兴虚假新闻的检测能力。