Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 32% schema drift. A deterministic paired simulation generated 72 cases and preserved a rare-condition slice. Mean execution accuracy changed from 0.501 to 0.544; the paired difference was +0.042 (95% interval +0.040 to +0.045). 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
Rehman, A. U. (2026). FinSight AI: Coupling a Large Language Model with a Live NoSQL Database for Conversational Financial Analytics and Rule-Assisted Fraud Screening. https://doi.org/10.2139/ssrn.7093099
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
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
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
Furniss, P., & Green, A. (2023). SQL/PGQ data model and graph schema. https://doi.org/10.54285/ldbc.qzsk3559
Chafik, S., Ezzini, S., & Berrada, I. (2025). Enhancing security in text-to-SQL systems: A novel dataset and agent-based framework. Natural Language Processing, 31(6), 1399-1422. https://doi.org/10.1017/nlp.2025.10008
Shamal Chavan and Prof. Sandeep Vishwakarma (2026). Natural Language to SQL (NL2SQL): A Comprehensive Study of Text-to-SQL Systems, Conversational AI for Databases, Enterprise Architectures, Challenges, and Future Directions. International Journal of Advanced Research in Science Communication and Technology, 380. https://doi.org/10.48175/ijarsct-37344
Beckmann, S., Wiesner, K., Tebruegge, C., & Grum, M. (2026). Combining Text-to-SQL and Large Language Models for Maintenance Decision Support. https://doi.org/10.2139/ssrn.7103027
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
