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English Information

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Authors
# Name
1 Caio Petroncini(caiopetroncini@usp.br)
2 Cauã Sathler(cauasathlerufmg@gmail.com)
3 João Calixto(jpcalixto@usp.br)
4 Jonas Melo(jonashonorato4@gmail.com)
5 Julio Fuganti(juliocesarfuganti@gmail.com)
6 Luiz dos Santos(luizgcorreiadosantos@usp.br)
7 Moacir Ponti(moacir@icmc.usp.br)

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Reference
# Reference
1 Yu, T. et al. (2018). Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. Proceedings of EMNLP 2018, 3911–3921.
2 Lei, F. et al. (2024). Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows. arXiv preprint arXiv:2411.07763
3 Gao, D. et al. (2024). DAIL-SQL: Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation. Proceedings of the VLDB Endowment, 17(5), 1132–1145.
4 Dong, X. et al. (2023). C3: Zero-shot Text-to-SQL with ChatGPT. arXiv preprint ar- Xiv:2307.07306
5 Pourreza, M. e Rafiei, D. (2024). DIN-SQL: Decomposed In-Context Learning of Text- to-SQL with Self-Correction. Advances in Neural Information Processing Systems, 36
6 Wang, B. et al. (2025). MAC-SQL: A Multi-Agent Collaborative Framework for Text-to- SQL. Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), 540–557.
7 Olist (2018). Brazilian E-Commerce Public Dataset by Olist. Disponível em: https: //www.kaggle.com/datasets/olistbr/brazilian-ecommerce.
8 LangGraph Documentation (2024). LangGraph: Build Stateful Multi-Actor Applications. Disponível em: https://langchain-ai.github.io/langgraph/.
9 Talaei, S., Pourreza, M., Chang, Y.-C., Mirhoseini, A., and Saberi, A. (2024). CHESS: Contextual Harnessing for Efficient SQL Synthesis. arXiv preprint ar- Xiv:2405.16755.
10 Li, H., Zhang, J., Liu, H., Fan, J., Zhang, X., Zhu, J., Wei, R., Pan, H., Li, C. e Chen, H. (2024). CodeS: Towards Building Open-Source Language Models for Text-to-SQL. Proceedings of the ACM on Management of Data, 2(3), 1–28