Tuning constraint-aware reranking for text-to-SQL execution under 15% schema drift: a hold-out check
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Keywords

text-to-SQL execution
constraint-aware reranking
schema drift
paired simulation
reproducibility

Abstract

We evaluated constraint-aware reranking for text-to-SQL execution under 15% schema drift. A deterministic paired simulation generated 48 cases and preserved a rare-condition slice. Mean execution accuracy changed from 0.563 to 0.617; the paired difference was +0.055 (95% interval +0.052 to +0.057). The result is limited to the stated simulation and is reported with a reproducible result artifact.

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References

Jiang, J., Shen, S., Xie, H., Li, Y., Shen, Y., Huang, D., Qian, B., Wu, Y., Zhang, W., Cui, B., & Chen, P. (2025). SQLGovernor: An LLM-powered SQL Toolkit for Real World Application. arXiv. https://doi.org/10.48550/arXiv.2509.08575

ŞAHİNASLAN, E., & ŞAHİNASLAN, Ö. (2022). Microsoft SQL Sunucusunda Veritabanı Kurtarma Teknikleri. International Journal of Innovative Engineering Applications, 6(1), 158-169. https://doi.org/10.46460/ijiea.1070325

Amar Kaygude, Onkar Rajguru, Sandesh Karad, & G.T.Avhad (2025). Text-to-SQL Conversion by using DeepLearning/Machine Learning: IntegratingNatural Language with Database Queries. international journal of engineering technology and management sciences, 9(3), 48-52. https://doi.org/10.46647/ijetms.2025.v09i03.009

Liang, Z., liu, L., Quan, R., zou, M., li, D., Tang, Y., & qin, H. (2026). Hierarchical Adaptive Reward-based Reinforcement Learning Model for High-Precision Text-to-SQL Generation. https://doi.org/10.2139/ssrn.6767042

Hairan, B., & Şahman, M. A. (2026). A comparative evaluation of large language models for detecting SQL injection vulnerabilities in web applications. PeerJ Computer Science, 12, e4015. https://doi.org/10.7717/peerj-cs.4015

Nugraha, G. P., Suadaa, L. H., Wilantika, N., & Maghfiroh, L. R. (2024). Pengembangan Aplikasi Chatbot dengan Large Language Model untuk Text-to-SQL Generation. Seminar Nasional Official Statistics, 2024(1), 831-840. https://doi.org/10.34123/semnasoffstat.v2024i1.2252

-, P. A., -, P. S., -, P. P., -, R. N., & -, P. K. N. (2024). QueryAI: A Conversational Interface for SQL Database Querying Using Natural Language Processing. International Journal For Multidisciplinary Research, 6(6), 30595. https://doi.org/10.36948/ijfmr.2024.v06i06.30595

Ma, X., Tian, X., Wu, L., Wang, X., Tang, X., & Wang, J. (2024). Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection. Frontiers in Artificial Intelligence and Applications. https://doi.org/10.3233/faia240949

Ascoli, B. G., & Choi, J. D. (2025). Advancing Conversational Text-to-SQL: Context Strategies and Model Integration with Large Language Models. Future Internet, 17(11), 527. https://doi.org/10.3390/fi17110527

Maleki, S. E., Pourreza, M., & Rafiei, D. (2026). Confidence Estimation for Text-to-SQL in Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 40(38), 32474-32482. https://doi.org/10.1609/aaai.v40i38.40523