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English Information

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Authors
# Name
1 João Victor Monteiro Macedo(joao.macedo@visagio.com)
2 Renato Miyaji(re.miyaji@usp.br)
3 Renato Moulin(renato.moulin@visagio.com)
4 Leonardo Machado(leonardo.machado@visagio.com)

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Reference
# Reference
1 Broder, A. Z. (1997). On the resemblance and containment of documents. Proceedings of the Compression and Complexity of Sequences, pages 21–29
2 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakan- tan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few- shot learners. In Advances in Neural Information Processing Systems (NeurIPS), vol- ume 33, pages 1877–1901.
3 Dong, X., Zhang, C., Ge, Y., Mao, Y., Gao, Y., Lin, J., Lou, D., et al. (2023). C3: Zero- shot text-to-SQL with ChatGPT. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP).
4 Floratou, A., Psallidas, F., Agrawal, A., et al. (2024). NL2SQL is not solved yet: Chal- lenges in real-world enterprise settings. In Proceedings of the VLDB Endowment, volume 17.
5 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.
6 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 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6943–6954. Association for Computational Linguistics.
7 Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Geng, R., Huo, N., Zhou, X., Ma, C., Li, G., Chang, K. C., Huang, F., Cheng, R., and Li, Y. (2024). Can LLM already serve as a database interface? a big bench for large-scale database grounded text-to-SQLs. In Advances in Neural Information Processing Systems (NeurIPS 2024)
8 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022). Training language models to follow in- structions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744.
9 Pourreza, M. and Rafiei, D. (2024). DIN-SQL: Decomposed in-context learning of text- to-SQL with self-correction. In Advances in Neural Information Processing Systems (NeurIPS 2024).
10 Pydantic AI Team (2024). Pydantic AI: Agent framework for model serving. https: //ai.pydantic.dev/.
11 Rajkumar, N., Li, R., and Bahdanau, D. (2022). Evaluating the text-to-SQL capabilities of large language models. arXiv preprint arXiv:2204.00498.
12 Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Ro- man, 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 Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3911–3921. Association for Computational Linguistics