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English Information

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Authors
# Name
1 Karina Fróes(karina.froes@ime.usp.br)
2 Kelly Rosa Braghetto(kellyrb@ime.usp.br)

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Reference
# Reference
1 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. Proc. VLDB Endow., 17(5):1132–1145.
2 Levene, M. and Loizou, G. (1999). Temporal relational databases. In A Guided Tour of Relational Databases and Beyond, pp. 385–408. Springer.
3 Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Geng, R., Huo, N., et al. (2023). 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, 36:42330–42357.
4 Özcan, F., Quamar, A., Sen, J., Lei, C., and Efthymiou, V. (2020). State of the art and open challenges in natural language interfaces to data. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2629–2636.
5 Pourreza, M. and Rafiei, D. (2024). DIN-SQL: Decomposed in-context learning of text-to-SQL with self-correction. Advances in Neural Information Processing Systems, 36.
6 Sun, R., Arik, S. Ö., Muzio, A., Miculicich, L., Gundabathula, S., Yin, P., Dai, H., Nakhost, H., Sinha, R., Wang, Z., et al. (2023). SQL-PaLM: Improved large language model adaptation for text-to-SQL (extended). ArXiv.
7 Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
8 Visperas, M., Adoptante, A. J., Borjal, C. J., Abia, M. T., Catapang, J. K., and Peramo, E. (2023). On modern text-to-SQL semantic parsing methodologies for natural language interface to databases: A comparative study. In 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 390–396. IEEE.
9 Wang, B., Shin, R., Liu, X., Polozov, O., and Richardson, M. (2019). RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers. ArXiv.
10 Xu, X., Liu, C., and Song, D. (2017). SQLNet: Generating structured queries from natural language without reinforcement learning. ArXiv.
11 Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018). Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. ArXiv.
12 Zhong, V., Xiong, C., and Socher, R. (2017). Seq2SQL: Generating structured queries from natural language using reinforcement learning. ArXiv.