Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 38% schema drift. A deterministic paired simulation generated 56 cases and preserved a median slice. Mean execution accuracy changed from 0.542 to 0.584; the paired difference was +0.043 (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
Petrola, L., Brayner, A., & Franco, W. (2025). Heuristic-Guided Text-to-SQL Translation with LLMs: Optimizing Natural Language Interfaces for Relational Databases. Anais do XL Simpósio Brasileiro de Banco de Dados (SBBD 2025), 126-139. https://doi.org/10.5753/sbbd.2025.247037
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
Ascoli, B. G., Kandikonda, Y. S. R., & Choi, J. D. (2025). ETM: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models. Future Internet, 17(8), 325. https://doi.org/10.3390/fi17080325
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
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
asadillah, A. (2019). PERANCANGAN DATABASE SISTEM PENJUALAN MENGGUNAKAN DELPHI DAN MICROSOFT SQL SERVER. https://doi.org/10.31219/osf.io/zat5b
Yuan, H., Tang, X., Chen, K., Shou, L., Chen, G., & Li, H. (2025). CogSQL: A Cognitive Framework for Enhancing Large Language Models in Text-to-SQL Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 39(24), 25778-25786. https://doi.org/10.1609/aaai.v39i24.34770
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
Rapolu, N. K. (2023). MIGRATION OF LEGACY DATABASE ORACLE/MS SQL TO HANA DATABASE TO IMPROVE REAL-TIME ONLINE ANALYTICAL PROCESSING AND ONLINE TRANSACTION PROCESSING FROM ONE DATA MODEL. International Scientific Journal of Engineering and Management, 02(03), 1-7. https://doi.org/10.55041/isjem00215