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English Information

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Authors
# Name
1 Robson Campêlo(robson.campelo@dcc.ufmg.br)
2 Alberto Laender(laender@dcc.ufmg.br)
3 Altigran da Silva(alti@icomp.ufam.edu.br)

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Reference
# Reference
1 Campelo, R. A., Laender, A. H. F., and Da Silva, A. S. (2023). Using Knowledge Graphs to Generate SQL Queries from Textual Specifications. In Sales, T. P., Araujo, J., Borbinha, J., and Guizzardi, G., editors, Advances in Conceptual Modeling, volume 14319, pages 85–94. Springer Nature Switzerland, Cham. Lecture Notes in Computer Science.
2 Dong, X., Zhang, C., Ge, Y., Mao, Y., Gao, Y., Chen, I., Lin, J., and Lou, D. (2023). C3: Zero-shot Text-to-SQL with ChatGPT.
3 Fensel, D., Şimşek, U., Angele, K., Huaman, E., Karle, E., Panasiuk, O., Toma, I., Umbrich, J., and Wahler, A. (2020). Knowledge Graphs: Methodology, Tools and Selected Use Cases. Springer International Publishing, Cham.
4 Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., and Zhou, J. (2024). Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation. Proceedings of the VLDB Endowment, 17(5):1132–1145.
5 Kim, H., So, B.-H., Han, W.-S., and Lee, H. (2020). Natural language to SQL: where are we today? Proceedings of the VLDB Endowment, 13(10):1737–1750.
6 Liu, X., Shen, S., Li, B., Ma, P., Jiang, R., Zhang, Y., Fan, J., Li, G., Tang, N., and Luo, Y. (2024). A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?
7 Nascimento, E. R., Avila, C. V. S., Izquierdo, Y. T., García, G. M., Andrade, L. F. L., Silva, M. O., Facina, M. S. P., Lemos, M., and Casanova, M. A. (2026). A Text-to-SQL strategy based on large language models and knowledge graphs for real-world databases. Data & Knowledge Engineering, 164:102580.
8 Pourreza, M. and Rafiei, D. (2023). DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction. In Advances in Neural Information Processing Systems, volume 36, pages 36339–36348.
9 Renze, M. (2024). The Effect of Sampling Temperature on Problem Solving in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7346–7356.
10 Xu, W., Zhu, H., Yan, L., Liu, C., Han, P., Duan, S., and Pan, J. Z. (2025). TS-SQL: Test-driven Self-refinement for Text-to-SQL. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 2864–2889.
11 Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., Zhang, Z., and Radev, D. (2018). Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3911–3921.
12 Zhang, H., Cao, R., Chen, L., Xu, H., and Yu, K. (2023). ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3501–3532.