Unsupervised Thematic Context Discovery for Explainable AI in Fact Verification: Advancing the CARAG Framework
Date
Authors
Vallayil, Manju
Nand, P
Yan, Wei Qi
Allende-Cid, H
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
SciTePress - Science and Technology Publications
Abstract
This 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.Description
Keywords
4613 Theory Of Computation, 46 Information and Computing Sciences, Explainable AI(XAI), Automated Fact Verification (AFV), Retrieval Augmented Generation (RAG), Explainable AFV, Fact Checking
Source
In 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: 10.5220/0013683400004000
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CC BY-NC-ND 4.0