Vallayil, ManjuNand, PYan, Wei QiAllende-Cid, HCoenen, FNolle, LAveiro, DFernández-Breis, JMasciari, EGruenwald, LBernardino, JTorres, R2025-12-112025-12-112025-12In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, 2025, Marbella, Spain; ISBN ; ISSN 2184-3228, SciTePress, pages 67-77. DOI: 10.5220/001368340000400097898975876962184-3228http://hdl.handle.net/10292/20397This paper introduces CARAG-u, an unsupervised extension of the Context-Aware Retrieval Augmented Generation (CARAG) framework, designed to advance explainability in Automated Fact Verification (AFV) architectures. Unlike its predecessor, CARAG-u eliminates reliance on predefined thematic annotations and claim-evidence pair labels, by dynamically deriving thematic clusters and evidence pools from unstructured datasets. This innovation enables CARAG-u to balance local and global perspectives in evidence retrieval and explanation generation. We benchmark CARAG-u against Retrieval Augmented Generation (RAG) and compare it with CARAG, highlighting its unsupervised adaptability while maintaining a competitive performance. Evaluations on the FactVer dataset demonstrate CARAG-u’s ability to generate thematically coherent and context-sensitive post-hoc explanations, advancing Explainable AI in AFV. The implementation of CARAG-u, including all dependencies, is publicly available to ensure reproducibility and support further research.CC BY-NC-ND 4.04613 Theory Of Computation46 Information and Computing SciencesExplainable AI(XAI)Automated Fact Verification (AFV)Retrieval Augmented Generation (RAG)Explainable AFVFact CheckingConference ContributionOpenAccess10.5220/0013683400004000