Explainable Retrieval-Augmented Generation Framework for Evidence-Aligned and Faithful Legal Reasoning

Abstract Retrieval-augmented generation (RAG) has emerged as an effective paradigm for legal question answering, but existing systems often suffer from hallucinated citations and weak evidence grounding. This paper proposes EL-RAG, an explainable retrieval-augmented generation framework designed to ensure evidence-aligned and faithful legal reasoning. The framework formulates evidence grounding as a probabilistic multi-objective optimization problem, integrating a hybrid sparse–dense retriever, a learning-to-rank reranker, a multi-hop reasoning generator, and an Evidence Alignment Layer (EAL). Two evaluation metrics—Citation Alignment Score (CAS) and Faithful Justification Index (FJI) are introduced to assess grounding and interpretability. Experiments on COLIEE 2025, NyayaRAG 2025, CaseLawBench, and LegalBench-RAG show consistent improvements over strong baselines, significantly reducing hallucinations and improving expert-rated interpretability.