CBR-to-SQL：重新思考基于检索的文本到SQL方法：在医疗领域使用基于案例的推理
CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
- Department of Computer Science Aalto University（奥卢大学计算机科学系）
- Department of Clinical Medicine Aalborg University（奥尔堡大学临床医学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出CBR-to-SQL框架，通过分解检索增强生成的两阶段检索，提升医疗领域文本到SQL的准确性和效率。
AI中文摘要：
从电子健康记录（EHR）数据库中提取见解通常需要SQL专业知识，这在临床决策和研究中形成障碍。一种有前景的方法是使用大型语言模型（LLMs）通过检索增强生成（RAG）将自然语言问题转换为SQL，其中相关问题-SQL示例被检索以通过少量样本学习生成新查询。然而，将此方法适应医疗领域具有挑战性，因为有效的检索需要示例与问题的逻辑结构及其参考实体（如药物名称、程序标题）对齐。标准单步RAG难以同时优化这两个方面，通常依赖近精确匹配来有效推广。在医疗领域，这个问题尤为严重，因为问题常包含噪声和不一致的医学术语。为了解决这个问题，我们提出了CBR-to-SQL，一个受案例推理理论启发的框架，将RAG的单步检索分解为两个显式阶段：一个专注于检索结构相关示例，另一个将实体与目标数据库模式对齐。在两个临床基准上评估，CBR-to-SQL在与微调方法竞争的准确性方面表现相当。更重要的是，它在数据稀缺和检索扰动下显示出显著更高的样本效率和鲁棒性。
英文摘要：
Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for clinical decision-making and research. A promising approach is to use Large Language Models (LLMs) to translate natural language questions into SQL through Retrieval-Augmented Generation (RAG), where relevant question-SQL examples are retrieved to generate new queries via few-shot learning. However, adapting this method to the medical domain is non-trivial, as effective retrieval requires examples that align with both the logical structure of the question and its referenced entities (e.g., drug names, procedure titles). Standard single-step RAG struggles to optimize both aspects simultaneously and often relies on near-exact matches to generalize effectively. This issue is especially severe in healthcare, as questions often contain noisy and inconsistent medical jargon. To address this, we present CBR-to-SQL, a framework inspired by Case-based Reasoning theory that decomposes RAG's single-step retrieval into two explicit stages: one that focuses on retrieving structurally relevant examples, and one that aligns entities with the target database schema. Evaluated on two clinical benchmarks, CBR-to-SQL achieves competitive accuracies compared to fine-tuned methods. More importantly, it demonstrates considerably higher sample efficiency and robustness than the standard RAG approach, particularly under data scarcity and retrieval perturbations.
