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English(EN) Residual Skill Optimization for Text-to-SQL Ensembles

DivSkill-SQL通过互补代理训练提升文本到SQL集成能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DivSkill-SQL，一个用于增强文本到SQL集成的创新框架。该方法通过在现有集成失败的示例上训练新代理来优化互补技能，从而提高生成至少一个正确SQL候选的概率。在Spider2-Lite数据集上使用Opus-4.6和GPT-5.4基础模型进行测试时，该框架在Snowflake上将准确率提高了11.1个百分点，在BigQuery上提高了8.3个百分点。值得注意的是，这些优化后的技能在不同SQL方言和任务表述之间表现出可迁移性，错误分析表明幻觉减少，互补技能更可靠。 AI

影响 提高了文本到SQL系统的准确性和可靠性，可能改善AI应用的数据访问和分析。

排序理由 该集群包含一篇学术论文，详细介绍了一种改进文本到SQL模型的新方法。[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 →

DivSkill-SQL通过互补代理训练提升文本到SQL集成能力

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该集群包含一篇学术论文，详细介绍了一种改进文本到SQL模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jiongli Zhu, Haoquan Guan, Parjanya Prajakta Prashant, Nikki Lijing Kuang, Seyedeh Baharan Khatami, Canwen Xu, Xiaodong Yu, Yingyu Lin, Zhewei Yao, Yuxiong He, Babak Salimi ·

    文本到SQL集成模型的残差技能优化

    arXiv:2605.21792v1 Announce Type: new Abstract: Text-to-SQL ensembles improve over single-candidate generation by drawing multiple SQL candidates and selecting one, but their effectiveness is bounded by Pass@K, the probability that at least one of K candidates is correct. Existin…

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