CARAG: A Context-Aware Retrieval Framework for Fact Verification, Integrating Local and Global Perspectives of Explainable AI
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Vallayil, Manju
Nand, Parma
Yan, Wei Qi
Allende-Cid, Héctor
Vamathevan, Thamilini
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MDPI AG
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This 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.
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Applied Sciences, ISSN: 2076-3417 (Online), MDPI AG, 15(4), 1970-1970. doi: 10.3390/app15041970
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Creative Commons Attribution (CC BY)
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. Open access.,
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. Open access.,
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Except where otherwise noted, this item's license is described as Creative Commons Attribution (CC BY)