Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 23% schema drift. A deterministic paired simulation generated 56 cases and preserved a upper-severity quartile. Mean execution accuracy changed from 0.498 to 0.535; the paired difference was +0.037 (95% interval +0.035 to +0.039). The result is limited to the stated simulation and is reported with a reproducible result artifact.
References
Xie, H., Zhou, X., Yang, J., Shen, S., Wang, Z., Zheng, Y., Xu, T., Shi, Y., Zong, Z., Li, Y., Chen, P., Jiang, J., He, D., Yan, X., & Jiang, J. (2026). SiriusDeliver: Automating Data Warehouse Delivery at Tencent. arXiv. https://doi.org/10.48550/arXiv.2608.09185
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
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
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
Mota, F. D. C., Silva, W. D. V. R. D., & Soares, J. D. N. (2026). BENCHMARK DE MODELOS DE LINGUAGEM DE CÓDIGO ABERTO PARA TEXT-TO-SQL EM DADOS ONCOLÓGICOS BRASILEIROS. REMUNOM, 13(14), 1-67. https://doi.org/10.66104/zvzdrv85
Shi, L., Tang, Z., Zhang, N., Zhang, X., & Yang, Z. (2026). A Survey on Employing Large Language Models for Text-to-SQL Tasks. ACM Computing Surveys, 58(2), 1-37. https://doi.org/10.1145/3737873
Jawale, D., Shelke, S., & Yadav, S. (2026). Analyzing the Impact of Database Architecture on Performance: SQL vs NoSQL. International Journal of Mathematics And Computer Research, 14(03). https://doi.org/10.47191/ijmcr/v14ispc3.13
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
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
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