无需复杂设计的迭代：简单的ReAct架构足以完成文本转SQL生成
Iteration Without Elaboration: A Simple ReAct Architecture Suffices for Text-to-SQL Generation
- Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对现有文本转SQL系统复杂且延迟高的问题，提出简单的ReAct-SQL框架，在两个数据集上达到高准确率且运行速度提升8倍，验证了其有效性。
AI中文摘要：
现代文本转SQL系统变得日益复杂，依赖模式链接模块、检索增强提示、候选生成以及多阶段优化流水线，虽有效却带来了显著的延迟和工程开销。为此，我们提出ReAct-SQL，这是一种简单却有效的零样本ReAct风格框架，仅基于迭代推理和由15种关系操作组成的类型化领域特定语言（DSL）定义的受限动作空间，而非自由形式的SQL生成。模型逐步发出DSL调用，观察编译后SQL的执行反馈，并通过交互修正推理。在修正后的BIRD mini-dev和EHR-SQL数据集上，ReAct-SQL分别达到84.5%和73.9%的准确率，与复杂得多的基线模型表现相当，同时运行速度最高快8倍。增量消融实验进一步表明，迭代主要提升了 grounding（ grounding可译为“ grounding：指模型将语言表述与实际数据或操作对应匹配的能力”），而DSL则提升了组合可靠性。
英文摘要：
Modern text-to-SQL systems have become increasingly elaborate, relying on schema-linking modules, retrieval-augmented prompting, candidate generation, and multi-stage refinement pipelines. While effective, these additions introduce substantial latency and engineering overhead. To this end, we present \textbf{ReAct-SQL}, a simple yet effective zero-shot ReAct-style framework built solely on iterative reasoning and a constrained action space defined by a typed Domain-Specific Language (DSL) of 15 relational operations, rather than free-form SQL generation. The model incrementally issues DSL calls, observes compiled-SQL execution feedback, and revises its reasoning through interaction. On corrected BIRD mini-dev and EHR-SQL, ReAct-SQL achieves \textbf{84.5\%} and \textbf{73.9\%} accuracy, respectively, matching substantially more elaborate baselines while running up to $8\times$ faster. Incremental ablations further show that iteration primarily improves grounding, while the DSL improves compositional reliability.
