VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation

Dongyu Wang, Jingyu Li, Lan Zhang, Ganggang.yu, Liang Huang


Abstract
Large language models (LLMs) have shown remarkable capabilities in text-to-SQL generation, yet existing approaches remain prone to hallucinations and lack verification mechanisms. Current methods such as Chain-of-Thought (CoT) and Program-of-Thought (PoT) typically rely on intermediate reasoning that is either purely textual or executed only as a final step, leaving the reasoning process opaque and prone to grounding and logical hallucinations. In this paper, we introduce Verifiable Execution Tracing (VET), a novel reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. VET addresses these limitations by constraining the reasoning process within a candidate schema space and formulating it as a sequence of executable Python steps. Crucially, each step is executed against the real database to produce observable intermediate results, which serve as immediate verification feedback and transform the traditionally opaque generation process into a transparent, debuggable interaction with database reality.Experiments show consistent gains under matched, training-free settings, achieving 70.93% execution accuracy on BIRD and 37.04% on Spider 2.0-lite, with particularly strong improvements on complex queries.
Anthology ID:
2026.findings-acl.1544
Volume:
Findings of the Association for Computational Linguistics: ACL 2026
Month:
July
Year:
2026
Address:
San Diego, California, United States
Editors:
Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
30867–30883
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URL:
https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1544/
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Cite (ACL):
Dongyu Wang, Jingyu Li, Lan Zhang, Ganggang.yu, and Liang Huang. 2026. VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30867–30883, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (Wang et al., Findings 2026)
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https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1544.pdf
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