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CAR-RAG: Category-Aware Hybrid Retrieval-Augmented Generation for Hallucination Mitigation
Tatiana V. Petrova (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust), Dmitrii Koriakov (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust), Radu State (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust)
Abstract: We introduce CAR-RAG (Category-Aware hybRid Retrieval-Augmented Generation), an approach to mitigate hallucinations in large language models (LLMs) for realworld deployments.Unlike single-modality pipelines, CAR-RAG conditions retrieval on semantic query categories and adaptively combines vector retrieval with lightweight causal query augmentation.This category-aware routing improves risk profiles by reducing contradicted claims, which are particularly harmful in safety-critical settings.We evaluate CAR-RAG on an automotive Q&A dataset comprising over 700 community-provided questions and answers from Stack Exchange's Motor Vehicle Maintenance & Repair forum.The framework achieves approximately 90% factual accuracy and reduces confidently incorrect statements to 6.4%, outperforming dense retrievers and the base LLM in risk-sensitive settings.These results highlight trade-offs between peak accuracy and robustness and position CAR-RAG as a practical, interpretable, deployment-ready solution for hallucination mitigation.It suits industrial contexts (automotive troubleshooting, service-center support, and technical diagnostics) where highprecision answers are essential.An open-source implementation is available on GitHub, 1 .