RoE-FND: A Case-Based Reasoning Approach with Dual Verification for Fake News Detection via LLMs

The proliferation of deceptive content online necessitates robust Fake News Detection (FND) systems. While evidence-based approaches leverage external knowledge to verify claims, existing methods face critical limitations: noisy evidence selection, generalization bottlenecks, and unclear decision-making processes. Recent efforts to harness Large Language Models (LLMs) for FND introduce new challenges, including hallucinated rationales and conclusion bias. To address these issues, we propose $\mathbf{\text{RoE-FND}}$ ($\underset{‾}{\mathbf{\text{R}}}$eason $\underset{‾}{\mathbf{\text{o}}}$n $\underset{‾}{\mathbf{\text{E}}}$xperiences FND), a framework that reframes evidence-based FND as a logical deduction task by synergizing LLMs with experiential learning. RoE-FND encompasses two stages: (1) $\mathit{\text{self-reflective}}\mathit{\text{knowledge}}\mathit{\text{building}}$, where a knowledge base is curated by analyzing past reasoning errors, namely the exploration stage, and (2) $\mathit{\text{dynamic}}\mathit{\text{criterion}}\mathit{\text{retrieval}}$, which synthesizes task-specific reasoning guidelines from historical cases as experiences during deployment. It further cross-checks rationales against internal experience through a devised dual-channel procedure. Key contributions include: a case-based reasoning framework for FND that addresses multiple existing challenges, a training-free approach enabling adaptation to evolving situations, and empirical validation of the framework’s superior generalization and effectiveness over state-of-the-art methods across three datasets.