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Authors
# Name
1 Eduardo Nascimento(rogerrsn@tecgraf.puc-rio.br)
2 Marco Antonio Casanova(casanova@inf.puc-rio.br)

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Reference
# Reference
1 Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). Qlora: Efficient finetuning of quantized llms. Available at: https://arxiv.org/abs/2305.14314.
2 Dong, X., Zhang, C., Ge, Y., Mao, Y., Gao, Y., lu Chen, Lin, J., and Lou, D. (2023). C3 zero-shot text-to-sql with chatgpt. Available at: ttps://arxiv.org/abs/2307.07306.
3 Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., and Zhou, J. (2023). Text-to-sql empowered by large language models a benchmark evaluation. Available at: https://arxiv.org/abs/2308.15363.
4 Groff, J. R. and Weinberg, P. N. (1999). SQL: The Complete Reference. Osborne/McGraw-Hill.
5 Guo, C., Tian, Z., Tang, J., Li, S., Wen, Z., Wang, K., and Wang, T. (2023). Retrievalaugmented gpt-3.5-based text-to-sql framework with sample-aware prompting and dynamic revision chain. Available at: https://arxiv.org/abs/2307.0
6 Langchain (2024). Langchain is a framework for developing applications powered by language models. Available at: https://python.langchain.com/docs/get_ started/introduction.
7 Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Cao, R., Geng, R., Huo, N., Zhou, X., Ma, C., Li, G., Chang, K. C. C., Huang, F., Cheng, R., and Li, Y. (2023). Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls. Available at: https://arxiv.org/abs/2305.03111.
8 May, W. (1999). Information extraction and integration with FLORID: The MONDIAL case study. Technical Report 131, Universitat Freiburg, Institut f ¨ ur Informatik. Availa- ¨ ble at: http://www.dbis.informatik.uni-goettingen.de/Mondial.
9 OpenAI (2024). Openai blog. Available at: https://openai.com/blog/ new-embedding-models-and-api-updates.
10 Pourreza, M. and Rafiei, D. (2023). Din-sql: Decomposed in-context learning of text-tosql with self-correction. Available at: https://arxiv.org/abs/2304.11015.
11 Quamar, A., Efthymiou, V., Lei, C., and Ozcan, F. (2022). Natural language interfaces to ¨ data. Foundations and Trends in Databases, 11(4):319–414.
12 Saravia, E. (2022). Prompt Engineering Guide. Available at: https://github.com/ dair-ai/Prompt-Engineering-Guide.
13 Singh, A. (2023). Large language models: A guide on its benefits, use cases, and types. Available at: https://yellow.ai/blog/large-language-models
14 Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, 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 Riloff, E., Chiang, D., Hockenmaier, J., and Tsujii, J., editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3911– 3921, Brussels, Belgium. Association for Computational Linguistics. Available at: https://aclanthology.org/D18-1425