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

ArXi:2603.05569v1 Announce Type: cross 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.