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English Information

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Authors
# Name
1 Thiago Braga(thiagobragaamorim@gmail.com)
2 Antônio Pereira(antoniopereira@aluno.ufsj.edu.br)
3 Mateus Brito(mateusdeoliveirabritoo@gmail.com)
4 Fabíola Simões(fabiola.oliveira8@gmail.com)
5 Raphael Aguiar(raphael.aguiar@gmail.com)
6 Juliana Ribeiro(julianapantuzavilar@gmail.com)
7 Leonardo Rocha(lcrocha@ufsj.edu.br)

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Reference
# Reference
1 Amazon Web Services (2024). Build a robust text-to-sql solution generating complex queries, self-correcting, and querying diverse data sources. AWS Machine Learning Blog. Acesso em: 20 jun. 2026.
2 Databricks (2024). Improving text2sql performance with ease on databricks. Databricks Blog. Acesso em: 20 jun. 2026.
3 de Almeida, E. C., Pena, E. H. M., and da Silva, A. S. (2024). This future without sql. In Proceedings of the 39th Brazilian Symposium on Databases (SBBD), Florianópolis, SC, Brazil. SBC.
4 Fróes, K. d. C. and Braghetto, K. R. (2025). Exploring temporal text-to-sql challenges in brazilian portuguese: Lessons from educational data. In Anais do 40º Simpósio Brasileiro de Banco de Dados (SBBD), pages 963–969, Fortaleza, CE. SBC
5 Leal, J. and Melegati, J. (2025). Enhancing text-to-sql with in-context learning: A multi- agent approach based on chess. In Anais do 40º Simpósio Brasileiro de Banco de Dados (SBBD), Fortaleza, CE. SBC
6 Lee, G., Kweon, S., Bae, S., and Choi, E. (2024). Overview of the ehrsql 2024 shared task on reliable text-to-sql modeling on electronic health records. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 644–654, Mexico City, Mexico. Association for Computational Linguistics.
7 Lei, W., Wang, W., Ma, Z., Gan, T., Lu, W., Kan, M.-Y., and Chua, T.-S. (2020). Re- examining the role of schema linking in text-to-sql. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6943–6954.
8 Luo, Y., Li, G., Fan, J., Chai, C., and Tang, N. (2025). Natural language to sql: State of the art and open problems. Proceedings of the VLDB Endowment, 18(12):5466–5471.
9 Nascimento, E. R. S. and Casanova, M. A. (2024). Querying databases with natural language: The use of large language models for text-to-sql tasks. Master’s thesis, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio). Dissertação de Mestrado.
10 Pedroso, B. C., Pereira, M. R., and Pereira, D. A. (2025). Performance evaluation of llms in the text-to-sql task in portuguese. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI), Recife, PE, Brazil.
11 Petrola, L., Brayner, A., and Franco, W. (2025). Heuristic-guided text-to-sql translation with llms: Optimizing natural language interfaces for relational databases. In Anais do 40º Simpósio Brasileiro de Banco de Dados (SBBD), pages 126–139, Fortaleza, CE.
12 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 (NeurIPS), volume 36.
13 Rajkumar, N., Li, R., and Bahdanau, D. (2022). Evaluating the text-to-sql capabilities of large language models. In Proceedings of the Workshop on Natural Language Proces- sing for Programming (NLP4Prog)