| 1 |
Centenaro, B. R. A. (2025). IA generativa para consultas SQL a partir de linguagem natural: Uma avaliação utilizando dados educacionais brasileiros. Trabalho
de Conclusão de Curso (Bacharelado), Universidade Federal de Santa Catarina
https://repositorio.ufsc.br/handle/123456789/266444.
|
|
| 2 |
de Carvalho, L. F. C., Júnior, P. S. d. S., and de Oliveira, H. T. A. (2025). Benchmarking
large language models for text-to-sql in brazilian portuguese and english. In Simposio ´
Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) , pages 101–
112.
|
|
| 3 |
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). Qlora: Efficient
finetuning of quantized llms. Advances in neural information processing systems,
36:10088–10115.
|
|
| 4 |
Dou, L., Gao, Y., Pan, M., Wang, D., Che, W., Zhan, D., and Lou, J.-G. (2023).
Multispider: towards benchmarking multilingual text-to-sql semantic parsing. In
Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages
12745–12753.
|
|
| 5 |
Fróes, K. and Braghetto, K. (2025). Exploring temporal text-to-sql challenges in
brazilian portuguese: Lessons from educational data. In Proceedings of the Brazilian
Symposium on Data Bases (SBBD), pages 963–969.
|
|
| 6 |
Gan, Y., Chen, X., Huang, Q., and Purver, M. (2022). Measuring and improving
compositional generalization in text-to-SQL via component alignment. In Findings
of the Association for Computational Linguistics: NAACL 2022, pages 831–843.
Association for Computational Linguistics (ACL).
|
|
| 7 |
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.
|
|
| 8 |
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A.,
Mathur, A., Schelten, A., Vaughan, A., et al. (2024). The llama 3 herd of models. arXiv
preprint arXiv:2407.21783.
|
|
| 9 |
Hui, B., Yang, J., Cui, Z., Yang, J., Liu, D., Zhang, L., Liu, T., Zhang, J., Yu, B., Lu, K.,
et al. (2024). Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186.
|
|
| 10 |
José, M. A. and Cozman, F. G. (2021). mrat-sql+ gap: a portuguese text-to-sql transformer. In Brazilian Conference on Intelligent Systems, pages 511–525. Springer.
|
|
| 11 |
José, M. A. and Cozman, F. G. (2023). A multilingual translator to sql with database
schema pruning to improve self-attention. International Journal of Information
Technology, 15(6):3015–3023.
|
|
| 12 |
Lei, F., Chen, J., Ye, Y., Cao, R., Shin, D., Su, H., Suo, Z., Gao, H., Hu, W., Yin, P., et al.
(2025). Spider 2.0: Evaluating language models on real-world enterprise text-to-sql
workflows. In International Conference on Learning Representations (ICLR), pages
28691–28735.
|
|
| 13 |
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.
|
|
| 14 |
Moraes, M., Figueiredo, I., Marques, V., Santos, J., and Manssour, I. H. (2025). From
questions to answers: A natural language interface for datasus hospitalization data. In
Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS), pages 296–299. SBC.
|
|
| 15 |
Pedroso, B. C., Pereira, M. R., and Pereira, D. A. (2025). Performance evaluation of llms
in the text-to-sql task in portuguese. In Simpósio Brasileiro de Sistemas de Informação
(SBSI), pages 260–269.
|
|
| 16 |
Petrola, L. and Franco, W. (2025). Heuristic-guided text-to-sql translation with llms:
Optimizing natural language interfaces for relational databases. In Proceedings of the
Brazilian Symposium on Data Bases (SBBD), pages 128–141.
|
|
| 17 |
Pham, K. T., Nguyen, T. H., Jo, J., Nguyen, Q. V. H., and Nguyen, T. T. (2025). Multilingual text-to-sql: Benchmarking the limits of language models with collaborative
language agents. In Australasian Database Conference, pages 108–123. Springer
|
|
| 18 |
Qwen Team (2025). Qwen2.5 technical report. arXiv preprint arXiv:2412.15115.
|
|
| 19 |
Song, Y., Wang, G., Li, S., and Lin, B. Y. (2025). The good, the bad, and the
greedy: Evaluation of llms should not ignore non-determinism. In Proceedings
of the Conference of the Nations of the Americas Chapter of the Association for
Computational Linguistics: Human Language Technologies (Volume 1: Long Papers),
pages 4195–4206.
|
|
| 20 |
Yamate, B. Y., Neubauer, T. R., Fantinato, M., and Peres, S. M. (2025). Text-to-sql
oriented to the process mining domain: A pt-en dataset for query translation. arXiv
preprint arXiv:2509.09684.
|
|
| 21 |
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. In Proceedings of the Conference
on Empirical Methods in Natural Language Processing, pages 3911–3921.
|
|