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English(EN) EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL

EvoSQL框架通过批评-生成器协同进化增强文本到SQL

研究人员开发了EvoSQL,一个新颖的框架,旨在通过将SQL合成视为生成器和批评者之间的迭代过程来增强文本到SQL的能力。该系统包含一个内存组件来存储和验证SQL候选,利用执行信号和基于LLM的批评进行优化。EvoSQL还包括一个自蒸馏策略优化(SDPO)阶段,以执行感知的监督来微调编码LLM,在Spider和BIRD等基准数据集上取得了显著改进,特别是对于Qwen3-4B和Qwen2.5-Coder-3B等开源模型。 AI

影响 这项研究可能带来更可靠和更具泛化能力的文本到SQL系统,改善用户通过自然语言与数据库的交互方式。

排序理由 该集群描述了一篇关于文本到SQL系统新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

EvoSQL框架通过批评-生成器协同进化增强文本到SQL

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该集群描述了一篇关于文本到SQL系统新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiawei Zhou, Jianwei Wang, Chenyu Zhou, Chaojian Shi, Ming Dong, Kai Wang ·

    EvoSQL:用于文本到SQL的增强记忆的Critic-Generator协同进化

    arXiv:2607.20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction. We…