Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 35% schema drift. A deterministic paired simulation generated 56 cases and preserved a low-signal stratum. Mean execution accuracy changed from 0.498 to 0.547; the paired difference was +0.049 (95% interval +0.047 to +0.051). 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
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
Jiang, G., Li, W., Yu, C., Zhu, Z., & LI, W. (2025). FGCSQL: A Three-Stage Pipeline for Large Language Model-driven Chinese Text-to-SQL. https://doi.org/10.20944/preprints202502.1059.v1
Zhou, F., Hu, S., Du, X., Li, N., Zhou, T., Zhao, Y., Shang, S., Ling, X., & Zhu, H. (2025). Nabil: A Text-to-SQL Model Based on Brain-Inspired Computing Techniques and Large Language Modeling. Electronics, 14(19), 3910. https://doi.org/10.3390/electronics14193910
Nayanakantha, B., Vidanage, K., Nirkhi, S., & Bhattacharyya, S. (2026). A Lightweight and Explainable Conversational AI Framework for Natural Language SQL Learning without Large Language Models. https://doi.org/10.21203/rs.3.rs-9823032/v1
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
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
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
Guo, M., & Sun, Y. (2026). A COMPARATIVE STUDY OF LLM - POWERED DATABASE INTERFACES VERSUS TRADITIONAL SQL SYSTEMS FOR INVENTORY MANAGEMENT. NLP & Text Mining, 11-21. https://doi.org/10.5121/csit.2026.160602
Reichenpfader, D., Müller, H., & Denecke, K. (2023). Large language model-based information extraction from free-text radiology reports: a scoping review protocol. https://doi.org/10.1101/2023.07.28.23292031