The questionable responses caused by knowledge hallucination may lead to LLMs' unstable ability in decision-making. However, it has never been investigated whether the LLMs' hallucination is possibly usable to generate negative reasoning for facilitating the detection of fake news. This study proposes a novel supervised self-reinforced reasoning rectification approach - SR$^3$ that yields both common reasonable reasoning and wrong understandings (negative reasoning) for news via LLMs reflection for semantic consistency learning. Upon that, we construct a negative reasoning-based news learning model called - \emph{NRFE}, which leverages positive or negative news-reasoning pairs for learning the semantic consistency between them. To avoid the impact of label-implicated reasoning, we deploy a student model - \emph{NRFE-D} that only takes news content as input to inspect the performance of our method by distilling the knowledge from \emph{NRFE}. The experimental results verified on three popular fake news datasets demonstrate the superiority of our method compared with three kinds of baselines including prompting on LLMs, fine-tuning on pre-trained SLMs, and other representative fake news detection methods.

本研究探讨了大型语言模型（LLM）产生的知识幻觉在假新闻检测中的可用性，填补了相关研究的空白。我们提出了一种新的监督自我增强推理纠正方法SR$^3$，通过语义一致性学习生成合理推理和错误理解，并构建了基于负向推理的新闻学习模型NRFE。实验结果表明，该方法在假新闻检测上优于多种基线方法，展示了其显著的提升潜力。

大型语言模型的幻觉可用性？基于大型语言模型的负向推理用于假新闻检测