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English Information

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Authors
# Name
1 Renato Miyaji(re.miyaji@usp.br)
2 Rafael Fernandes(rafael.macario@usp.br)
3 Krysthian Martins(krysthian@usp.br)
4 Jorge Melegati(jorge@jmelegati.com)
5 Pedro Corrêa(pedro.correa@usp.br)

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Reference
# Reference
1 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. ArXiv.
2 Hong, Z., Yuan, Z., Zhang, Q., Chen, H., Dong, J., Huang, F., and Huang, X. (2024). Next-generation database interfaces: A survey of llm-based text-to-sql. ArXiv.
3 Katsogiannis-Meimarakis, G. and Koutrika, G. (2023). A survey on deep learning approaches for text-to-sql. The VLDB Journal, 32:905—-936.
4 Lewis, P., Perez, E., Piktus, A., Petroni, F., and Karpukhin, V. (2020). Retrieval- augmented generation for knowledge-intensive nlp tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, Red Hook, NY, USA. Curran Associates Inc.
5 Li, J., Hui, B., Cheng, R., Qin, B., and Ma, C. (2023). Graphix-t5: mixing pre-trained transformers with graph-aware layers for text-to-sql parsing. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press.
6 Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Geng, R., Huo, N., et al. (2024). Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls. Advances in Neural Information Processing Systems, 36.
7 Nascimento, E. and Casanova, M. A. (2024). Querying databases with natural language: The use of large language models for text-to-sql tasks. In Anais Estendidos do XXXIX Simposio Brasileiro de Bancos de Dados ´ , pages 196–201, Porto Alegre, RS, Brasil. SBC.
8 OpenAI (2025). Gpt-4o. Available on: https://platform.openai.com/docs/ models/gpt-4o. Accessed on 21 April 2025.
9 Pourreza, M. and Rafiei, D. (2023). Din-sql: decomposed in-context learning of text-to- sql with self-correction. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA. Curran Associates Inc.
10 Talaei, S., Pourreza, M., Chang, Y., Mirhoseini, A., and Saberi, A. (2024). Chess: Con- textual harnessing for efficient sql synthesis. ArXiv.
11 Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA. Curran Associates Inc.
12 Yin, P., Neubig, G., Yih, W., and Riedel, S. (2020). Tabert: Pretraining for joint under- standing of textual and tabular data. In Proceeding of the 58th Annual Meeting of the Association for Computational Linguistics.
13 Yu, T., Zhang, R., Yang, K., Yasunaga, M., and Wang, D. (2018). Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to- SQL task. In Riloff, E., editor, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3911–3921, Brussels, Belgium. As- sociation for Computational Linguistics.