SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Eduardo Bezerra(ebezerra@cefet-rj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Anderson, P. W. (1972). More is different. Science, 177(4047):393–396.
2 Arslan, M., Ghanem, H., Munawar, S., and Cruz, C. (2024). A survey on RAG with llms. Procedia Computer Science, 246:3781–3790. 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024).
3 Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3:1137–1155.
4 Bezerra, E. (2016). Introdução à aprendizagem profunda. In Ogasawara, V., editor, Tópicos em Gerenciamento de Dados e Informações, chapter 3, pages 57–86. SBC, Porto Alegre, Brazil, 1 edition.
5 Deng, N., Chen, Y., and Zhang, Y. (2022). Recent advances in text-to-SQL: A survey of what we have and what we expect. In COLING, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
6 Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., and Solorio, T., editors, ACL 2019, pages 4171–4186, Minneapolis, Minnesota. ACL.
7 Erdogan, L. E., Furuta, H., Kim, S., et al. (2025). Plan-and-act: Improving planning of agents for longhorizon tasks. In ICML 2025.
8 Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., and Li, Q. (2024). A survey on RAG meeting llms: Towards retrieval-augmented large language models. In KDD’24, KDD’24, page 64916501, New York, NY, USA. Association for Computing Machinery.
9 Hu, S., Kim, S. R., Zhang, Z., et al. (2025). Pre-act: Multistep planning and reasoning improves acting in LLM agents. arXiv preprint arXiv:2505.09970.
10 Jennings, N. R. and Wooldridge, M. J., editors (1998). Agent Technology: Foundations, Applications, and Markets. Springer, Berlin, Heidelberg.
11 Kayhan, V., Levine, S., Nanda, N., Schaeffer, R., Natarajan, A., Chughtai, B., et al. (2023). Scaling laws and emergent capabilities of large language models. arXiv preprint arXiv:2309.00071.
12 LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
13 Liu, X., Shen, S., Li, B., Ma, P., Jiang, R., Zhang, Y., Fan, J., Li, G., Tang, N., and Luo, Y. (2025). A survey of text-to-sql in the era of llms: Where are we, and where are we going? IEEE Transactions on Knowledge and Data Engineering, pages 1–20.
14 Mikolov, T., Karafiát, M., Burget, L., Cernock`y, J., and Khudanpur, S. (2010). Recurrent neural network based language model. In INTERSPEECH, pages 1045–1048.
15 Newell, A., Shaw, J., and Simon, H. A. (1956). The logic theory machine–a complex information processing system. IRE Transactions on Information Theory, 2(3):61–79.
16 Nilsson, N. J. (1984). Shakey the Robot. SRI International, Menlo Park, CA.
17 Rawat, M., Gupta, A., et al. (2025). Preact: Multistep planning and reasoning improves acting in llm agents. arXiv preprint arXiv:2505.09970.
18 Rosenfeld, R. (2000). Two decades of statistical language modeling: where do we go from here? Proceedings of the IEEE, 88(8):1270–1278.
19 Russell, S. and Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson, 4th edition.
20 Sapkota, R., Roumeliotis, K. I., and Karkee, M. (2025). AI agents vs. agentic ai: A conceptual taxonomy, applications and challenges.
21 Shi, L., Tang, Z., Zhang, N., Zhang, X., and Yang, Z. (2025). A survey on employing large language models for text-to-sql tasks. ACM Comput. Surv.
22 Shorten, C., Pierse, C., Smith, T. B., D’Oosterlinck, K., Celik, T., Cardenas, E., Monigatti, L., Hasan, M. S., Schmuhl, E., Williams, D., Kesiraju, A., and van Luijt, B. (2025). Querying databases with function calling.
23 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), pages 5998–6008.
24 Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T. B., Vinyals, O., Liang, P., Dean, J., and Fedus, W. (2022a). Emergent abilities of large language models. arXiv preprint arXiv:2206.07682.
25 Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2022b). Chain-of-thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903.
26 Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, C., and Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601.
27 Yao, S., Zhao, J., Yu, D., Du, N., Yu, W.-t., Shafran, I., Griffiths, T. L., Neubig, G., Cao, C., and Narasimhan, K. (2022). React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629.