GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL

• URL: http://arxiv.org/abs/2509.12612v1
• Date: Tue, 16 Sep 2025 03:21:12 GMT
• Title: GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL
• Authors: Daojun Chen, Xi Wang, Shenyuan Ren, Qingzhi Ma, Pengpeng Zhao, An Liu,
• Abstract summary: GBV- is a novel multi-agent framework that introduces Guided Generation with SQL2Text Back-translation Validation.<n>This mechanism uses a specialized agent to translate the generatedsql back into natural language, which verifies its logical alignment with the original question.<n>We introduce a formal typology for "Gold Errors", which are pervasive flaws in the ground-truth and demonstrate how they obscure true model performance.
• Score: 12.455525963127497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Large Language Models have significantly advanced Text2SQL generation, a critical semantic gap persists where syntactically valid queries often misinterpret user intent. To mitigate this challenge, we propose GBV-SQL, a novel multi-agent framework that introduces Guided Generation with SQL2Text Back-translation Validation. This mechanism uses a specialized agent to translate the generated SQL back into natural language, which verifies its logical alignment with the original question. Critically, our investigation reveals that current evaluation is undermined by a systemic issue: the poor quality of the benchmarks themselves. We introduce a formal typology for "Gold Errors", which are pervasive flaws in the ground-truth data, and demonstrate how they obscure true model performance. On the challenging BIRD benchmark, GBV-SQL achieves 63.23% execution accuracy, a 5.8% absolute improvement. After removing flawed examples, GBV-SQL achieves 96.5% (dev) and 97.6% (test) execution accuracy on the Spider benchmark. Our work offers both a robust framework for semantic validation and a critical perspective on benchmark integrity, highlighting the need for more rigorous dataset curation.

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