Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 8% schema drift. A deterministic paired simulation generated 64 cases and preserved a boundary-condition stratum. Mean execution accuracy changed from 0.555 to 0.596; the paired difference was +0.041 (95% interval +0.039 to +0.044). 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
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
C. Joel, U., & Dewole A. Temilola, J. (2025). AN ENHANCED NATURAL LANGUAGE PROCESSING MODEL FOR DATA EXTRACTION AND VISUALIZATION FROM SQL DATABASE STRUCTURES: A SYSTEMATIC REVIEW OF LITERATURE. Jana Nexus: Journal of Computer Science, 01(12), 26-31. https://doi.org/10.21474/jncs01/111
Utomo, M. N. Y. (2020). Pengembangan Model Migrasi Database Relational ke NoSQL Memanfaatkan Metadata SQL. Jurnal Teknologi Elekterika, 4(2), 1. https://doi.org/10.31963/elekterika.v4i2.2212
Amel Abdyssalam A Alhaag (2025). Comparison between Database Search Algorithms (SQL and no SQL). مجلة العلوم الشاملة, 9(ملحق 36), 1810-1830. https://doi.org/10.65405/bc8atc11
Furniss, P., & Green, A. (2023). SQL/PGQ data model and graph schema. https://doi.org/10.54285/ldbc.qzsk3559
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
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
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
Narasimhan, A., Bhamboo, A. K., Devnathan, A., Vellaisamy, J., & Vijayaraghavan, V. (2024). Benchmarking Large Language Models for NL-to-SQL: A Comprehensive Evaluation of Accuracy, Cost and Throughput. https://doi.org/10.36227/techrxiv.173121325.56335825/v1