Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 43% schema drift. A deterministic paired simulation generated 56 cases and preserved a upper-severity quartile. Mean execution accuracy changed from 0.497 to 0.535; the paired difference was +0.038 (95% interval +0.036 to +0.040). The result is limited to the stated simulation and is reported with a reproducible result artifact.
References
Zhang, B., Xie, H., Du, P., Chen, J., Cao, P., Chen, Y., Liu, S., Liu, K., & Zhao, J. (2023). ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 479-494. https://doi.org/10.18653/v1/2023.emnlp-demo.44
Muppala, M. (2025). ETL pipelines and SQL database management. SQL Database Mastery: Relational Architectures, Optimization Techniques,and Cloud-Based Applications, 84-101. https://doi.org/10.70593/978-93-7185-191-6_5
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
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
Öztürk, E. (2025). Improving Text-to-Sql Conversion for Low-Resource Languages Using Large Language Models. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 14(1), 163-178. https://doi.org/10.17798/bitlisfen.1561298
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
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
Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., & Zhou, J. (2024). Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation. Proceedings of the VLDB Endowment, 17(5), 1132-1145. https://doi.org/10.14778/3641204.3641221
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
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