Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 36% schema drift. A deterministic paired simulation generated 56 cases and preserved a rare-condition slice. Mean execution accuracy changed from 0.541 to 0.591; the paired difference was +0.050 (95% interval +0.047 to +0.052). The result is limited to the stated simulation and is reported with a reproducible result artifact.
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
Jiang, G., Li, W., Yu, C., Zhu, Z., & LI, W. (2025). FGCSQL: A Three-Stage Pipeline for Large Language Model-driven Chinese Text-to-SQL. https://doi.org/10.20944/preprints202502.1059.v1
Furniss, P., & Green, A. (2023). SQL/PGQ data model and graph schema. https://doi.org/10.54285/ldbc.qzsk3559
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
Mukti, R. F., Prasetya, A., & Cahyono, T. A. (2026). Deteksi Ill-Formed Natural Language Query Berbasis Rule Pada Model Llm Text-to-SQL Berbahasa Indonesia. HORIZON: Indonesian Journal of Multidisciplinary, 4(4), 5088-5098. https://doi.org/10.54373/hijm.v4i4.6916
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
Kim, H., Kim, W., & Kim, W. (2026). GRASP-SQL: Graph Retrieval and Agentic Schema Pruning for Recall-First Text-to-SQL. https://doi.org/10.2139/ssrn.7194451
Amel Abdyssalam A Alhaag (2025). Comparison between Database Search Algorithms (SQL and no SQL). مجلة العلوم الشاملة, 9(ملحق 36), 1810-1830. https://doi.org/10.65405/bc8atc11
Wang, Y., Chen, Y., Chen, R., Shu, H., Liu, P., & Xu, W. (2026). Enhanced Financial Text-to-SQL Generation via Fine-Grained SQL Refinement. https://doi.org/10.2139/ssrn.6502095
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