English(EN) RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning

RingSQL框架生成合成数据以增强文本到SQL模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-06 04:00

研究人员开发了RingSQL，一个用于生成合成问题-SQL对以改进文本到SQL模型的新型混合框架。该方法结合了模式无关的查询模板和基于LLM的问题释义，确保正确性的同时扩展数据生成。RingSQL的合成数据集在RLVR训练中表现出改进的性能，在Spider和BIRD等基准测试中达到了69.8%的平均准确率，并优于现有的合成数据集和人工标注数据。 AI

影响增强了文本到SQL模型的合成数据生成，可能提高性能并减少对手动创建数据集的依赖。

排序理由该集群描述了一篇关于文本到SQL领域新颖合成数据生成框架的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 该集群描述了一篇关于文本到SQL领域新颖合成数据生成框架的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Marko Sterbentz, Kevin Cushing, Cameron Barrie, Kristian J. Hammond · 2026-08-06 04:00

RingSQL: 文本到SQL强化学习的独立于模式的合成数据生成

arXiv:2601.05451v2 Announce Type: replace-cross Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR. However, progress remains limited by scarce high-quality training data, a problem RLVR is especially se…

本文如何被排名

报道来源 [1]

RingSQL: 文本到SQL强化学习的独立于模式的合成数据生成

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