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English Information

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Authors
# Name
1 Antony Seabra(amede@bndes.gov.br)
2 Claudio Cavalcante(cfrag@bndes.gov.br)
3 João Nepomuceno(jonep@bndes.gov.br)
4 Lucas Lago(lulag@bndes.gov.br)
5 Nicolaas Ruberg(nic@bndes.gov.br)
6 Sérgio Lifschitz(sergio@inf.puc-rio.br)

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Reference
# Reference
1 Chen, J., Lin, H., Han, X., and Sun, L. (2024). Benchmarking large language models in retrieval-augmented generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 17754–17762.
2 Feng, Z., Feng, X., Zhao, D., Yang, M., and Qin, B. (2024). Retrieval-generation synergy augmented large language models. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 11661–11665. IEEE.
3 Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., and Zhou, J. (2023a). Text-to-sql empowered by large language models: A benchmark evaluation. arXiv preprint arXiv:2308.15363.
4 Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., and Wang, H. (2023b). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
5 Giray, L. (2023). Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633.
6 Jeong, C. (2023). A study on the implementation of generative ai services using an enterprise databased llm application architecture. arXiv preprint arXiv:2309.01105.
7 Li, H., Su, Y., Cai, D., Wang, Y., and Liu, L. (2022). A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110.
8 Liu, A., Hu, X., Wen, L., and Yu, P. S. (2023). A comprehensive evaluation of chatgpt’s zero-shot text-tosql capability. arXiv preprint arXiv:2303.13547.
9 OpenAI (2023a). Chatgpt fine-tune description. https://help.openai.com/en/articles/6783457-what-ischatgpt. Accessed: 2024-03-01.
10 OpenAI (2023b). Chatgpt prompt engineering. https://platform.openai.com/docs/guides/promptengineering. Accessed: 2024-04-01.
11 Pinheiro, J., Victorio, W., Nascimento, E., Seabra, A., Izquierdo, Y., Garcıa, G., Coelho, G., Lemos, M., Leme, L. A. P. P., Furtado, A., et al. (2023). On the construction of database interfaces based on large language models. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, pages 373–380. INSTICC, SciTePress.
12 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
13 Wang, M., Wang, M., Xu, X., Yang, L., Cai, D., and Yin, M. (2023). Unleashing chatgpt’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering. IEEE Transactions on Learning Technologies.
14 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.