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English(EN) SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation

新的SQLMorph框架通过查询变异和细粒度指标增强文本到SQL评估

研究人员推出SQLMorph,一个旨在改进文本到SQL系统评估的新框架。该框架采用查询变异技术,包括连接查询扩展(JQE)和文本查询增强(TQA),以自动生成和扩展评估数据集。SQLMorph还提出了一套执行级别的指标,如执行精度(EXP)和执行召回率(EXR),与传统的二元指标相比,它们提供了对系统性能更细致的分析。 AI

影响 提高了文本到SQL系统评估的可靠性和可复现性,这对于推动该领域的发展至关重要。

排序理由 该集群包含一篇研究论文,详细介绍了用于评估文本到SQL系统的新框架和方法。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SQLMorph框架通过查询变异和细粒度指标增强文本到SQL评估

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该集群包含一篇研究论文,详细介绍了用于评估文本到SQL系统的新框架和方法。 [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) · Mohammadhossein Malekpour, Mohamed Riahi, Maxime Lamothe, Amine Mhedhbi ·

    SQLMorph:文本到SQL评估的查询变异和细粒度指标

    arXiv:2609.08950v1 Announce Type: cross Abstract: Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven by large language models (LLMs), evaluation remains a major bottleneck: public ben…