A Hybrid Framework Integrating LLM and ANFIS for Explainable Fact-Checking

The widespread utilization of social media for information consumption has significantly exacerbated the problem of information disorder. Recognizing the difficulty people face in discerning the truth, automated assistance is urgently needed. Current state-of-the-art approaches often involve fine-tuning existing models with contributions from domain knowledge bases. The black-box nature and interpretability issues of deep neural networks (DNNs) have increased interest in hybrid approaches, giving rise to Deep Neural Fuzzy Systems (DNFSs). This research paper presents the Hybrid Fact-Checking Framework (HFCF) leveraging a DNFS tailored to address the uncertainty inherent in fact verification tasks and enhance the reliability of model responses. The DNFS integrates a Large Language Model (LLM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS) for automated fact verification. The framework utilizes relevant evidence from open-world and closed-world sources, leveraging deep language models and employing few-shot prompting without additional training to generate and justify verdicts. Including fuzzy rules and considering the trustworthiness and relevance of retrieved evidence enhances response reliability, thereby improving overall effectiveness and outcome interpretability. Experimental validations have been conducted on three publicly available datasets ranging in different domains of expertise: ClimateFEVER, SciFact, and FEVER. The results demonstrate that the proposed framework ensures better outcomes, transparency, and mindful decision-making.