SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis
arXiv AIArchived 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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✦ AI Summary· Claude Sonnet
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
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Submission history
From: Zhenping Li [view email]
[v1] Thu, 23 Apr 2026 08:27:43 UTC (606 KB)
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