CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain

• URL: http://arxiv.org/abs/2603.05569v1
• Date: Thu, 05 Mar 2026 14:43:14 GMT
• Title: CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
• Abstract summary: We introduce CBR-to-based Reasoning (CBR), a framework inspired by Case-Based Reasoning (CBR)<n>CBR represents question- pairs as reusable, abstract case templates and utilizes a two-stage retrieval process that first captures logical structure and then resolves relevant entities.<n>It demonstrates higher sample efficiency and robustness than standard RAG approaches, particularly under data scarcity and retrievals.
• Score: 11.906108164120864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for healthcare decision-making and research. While a promising approach is to use Large Language Models (LLMs) to translate natural language questions to SQL via Retrieval-Augmented Generation (RAG), adapting this approach to the medical domain is non-trivial. Standard RAG relies on single-step retrieval from a static pool of examples, which struggles with the variability and noise of medical terminology and jargon. This often leads to anti-patterns such as expanding the task demonstration pool to improve coverage, which in turn introduces noise and scalability problems. To address this, we introduce CBR-to-SQL, a framework inspired by Case-Based Reasoning (CBR). It represents question-SQL pairs as reusable, abstract case templates and utilizes a two-stage retrieval process that first captures logical structure and then resolves relevant entities. Evaluated on MIMICSQL, CBR-to-SQL achieves state-of-the-art logical form accuracy and competitive execution accuracy. More importantly, it demonstrates higher sample efficiency and robustness than standard RAG approaches, particularly under data scarcity and retrieval perturbations.

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