Vallayil, ManjuManjuVallayilNand, ParmaParmaNandYan, WeiqiWeiqiYanAllende-Cid, HéctorHéctorAllende-CidVamathevan, ThamiliniThamiliniVamathevan2025-04-072025-04-072025https://publica.fraunhofer.de/handle/publica/48625410.3390/app150419702-s2.0-85218622156This study introduces an explainable framework for Automated Fact Verification (AFV) systems, integrating a novel Context-Aware Retrieval and Explanation Generation (CARAG) methodology. CARAG enhances evidence retrieval by leveraging thematic embeddings derived from a Subset of Interest (SOI, a focused subset of the fact-verification dataset) to integrate local and global perspectives. The retrieval process combines these thematic embeddings with claim-specific vectors to refine evidence selection. Retrieved evidence is integrated into an explanation-generation pipeline employing a Large Language Model (LLM) in a zero-shot paradigm, ensuring alignment with topic-based thematic contexts. The SOI and its derived thematic embeddings, supported by a visualized SOI graph, provide transparency into the retrieval process and promote explainability in AI by outlining evidence-selection rationale. CARAG is evaluated using FactVer, a novel explanation-focused dataset curated to enhance AFV transparency. Comparative analysis with standard Retrieval-Augmented Generation (RAG) demonstrates CARAG’s effectiveness in generating contextually aligned explanations, underscoring its potential to advance explainable AFV frameworks.entrueAFVautomated fact verificationexplainable AIglobal explainabilityRAGXAIjournal article