Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 33% schema drift. A deterministic paired simulation generated 64 cases and preserved a low-signal stratum. Mean execution accuracy changed from 0.483 to 0.528; the paired difference was +0.046 (95% interval +0.043 to +0.048). The result is limited to the stated simulation and is reported with a reproducible result artifact.
References
Jiang, J., Xie, H., Shen, S., Shen, Y., Zhang, Z., Lei, M., Zheng, Y., Li, Y., Li, C., Huang, D., Wu, Y., Zhang, W., Cui, B., & Chen, P. (2025). SiriusBI: A Comprehensive LLM-Powered Solution for Data Analytics in Business Intelligence. Proceedings of the VLDB Endowment, 18(12), 4860-4873. https://doi.org/10.14778/3750601.3750610
Wang, Y., Lv, H., & Qian, Y. (2026). CIR-SQL: A Dual-Model Intent Recognition Framework for Chinese Text-to-SQL. AI, 7(3), 91. https://doi.org/10.3390/ai7030091
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
Cinquin, O. (2024). Steering veridical large language model analyses by correcting and enriching generated database queries: first steps toward ChatGPT bioinformatics. Briefings in Bioinformatics, 26(1), bbaf045. https://doi.org/10.1093/bib/bbaf045
Utomo, M. N. Y. (2020). Pengembangan Model Migrasi Database Relational ke NoSQL Memanfaatkan Metadata SQL. Jurnal Teknologi Elekterika, 4(2), 1. https://doi.org/10.31963/elekterika.v4i2.2212
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
Zhou, X., Sun, Z., & Li, G. (2024). DB-GPT: Large Language Model Meets Database. Data Science and Engineering, 9(1), 102-111. https://doi.org/10.1007/s41019-023-00235-6
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
C. Joel, U., & Dewole A. Temilola, J. (2025). AN ENHANCED NATURAL LANGUAGE PROCESSING MODEL FOR DATA EXTRACTION AND VISUALIZATION FROM SQL DATABASE STRUCTURES: A SYSTEMATIC REVIEW OF LITERATURE. Jana Nexus: Journal of Computer Science, 01(12), 26-31. https://doi.org/10.21474/jncs01/111
Mellah, Y., Kocaman, V., Ul Haq, H., & Talby, D. (2024). Efficient schema-less text-to-SQL conversion using large language models. Artificial Intelligence in Health, 1(2), 96. https://doi.org/10.36922/aih.2661
