Leveraging ensemble LLMs and contextual embeddings for case-based reasoning in the legal domain.

This research investigates the integration of Case-Based Reasoning (CBR) with Retrieval-Augmented Generation (RAG) for Large Language Models (LLMs) to enhance the reliability of legal question-answering systems. Thus far, we have developed a structured retrieval mechanism using CBR to improve the contextual relevance of generative outputs. Additionally, we introduced two novel alignment-based evaluation metrics—weighted and unweighted—which demonstrated superior performance over existing baselines in assessing QA responses. Our experimental validation on a legal dataset confirmed the effectiveness of the CBR-RAG approach in improving response accuracy. Moving forward, we aim to refine weighting strategies for alignment metrics and enhance textual representations to improve evaluation robustness. Furthermore, we plan to extend our study beyond the legal domain by conducting a comparative analysis across multiple datasets, ensuring broader applicability of the CBR-RAG framework.