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SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis

arXiv AI Archived Apr 24, 2026 ✓ Full text saved

arXiv:2604.21414v1 Announce Type: new Abstract: Existing text-to-SQL synthesis pipelines still conflate executability with semantic validity: syntactic checks and execution-based validation can retain queries that execute successfully while violating database semantics. To address these limitations, we propose SemanticAgent, a semantic-aware synthesis framework. SemanticAgent organizes synthesis around three specialized modules: an analyzer, a synthesizer, and a verifier. Through a three-stage p

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    Computer Science > Artificial Intelligence [Submitted on 23 Apr 2026] SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis Qiang Gao, Zhenping Li, Anqi Zhuo, Yingxiao Zhao, Weibo Geng, Xiaosong Li Existing text-to-SQL synthesis pipelines still conflate executability with semantic validity: syntactic checks and execution-based validation can retain queries that execute successfully while violating database semantics. To address these limitations, we propose SemanticAgent, a semantic-aware synthesis framework. SemanticAgent organizes synthesis around three specialized modules: an analyzer, a synthesizer, and a verifier. Through a three-stage protocol of semantic analysis, stepwise synthesis, and diagnostic refinement, SemanticAgent transforms execution-based validation alone into a traceable reasoning process. Our framework generates synthetic data that consistently outperforms prior synthesis methods under semantic-quality evaluation, leading to stronger downstream fine-tuning performance, especially on semantically demanding benchmarks. Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.7; I.2.4 Cite as: arXiv:2604.21414 [cs.AI]   (or arXiv:2604.21414v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21414 Focus to learn more Submission history From: Zhenping Li [view email] [v1] Thu, 23 Apr 2026 08:27:43 UTC (606 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 24, 2026
    Archived
    Apr 24, 2026
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