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 Luis Azevedo(lcdeazevedo@gmail.com)
2 Arthur Machado(art.pmac@gmail.com)
3 Ronaldo Prati(ronaldo.prati@ufabc.edu.br)

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

Reference
# Reference
1 Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
2 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. NIPS’2020, 33:1877–1901.
3 Dahl, M., Magesh, V., Suzgun, M., and Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. J. Leg. Anal,, 16(1):64–93.
4 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.
5 Gu, J., Jiang, X., Shi, Z., Tan, H., Zhai, X., Xu, C., Li, W., Shen, Y., Ma, S., Liu, H., et al. (2024). A survey on llm-as-a-judge. The Innovation.
6 Guo, Z., Xia, L., Yu, Y., Ao, T., and Huang, C. (2024). Lightrag: Simple and fast retrieval-augmented generation. ArXiv, abs/2410.05779.
7 Han, H., Shomer, H., Wang, Y., Lei, Y., Guo, K., Hua, Z., Long, B., Liu, H., and Tang, J. (2025). Rag vs. graphrag: A systematic evaluation and key insights. ArXiv, abs/2502.11371.
8 Han, H., Wang, Y., Shomer, H., Guo, K., Ding, J., Lei, Y., Halappanavar, M., Rossi, R. A., Mukherjee, S., Tang, X., He, Q., Hua, Z., Long, B., Zhao, T., Shah, N., Javari, A., Xia, Y., and Tang, J. (2024). Retrieval-augmented generation with graphs (graphrag). ArXiv, abs/2501.00309.
9 Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Comput Surv., 54(4):1–37.
10 Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., and Liu, T. (2023). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Trans. Inf. Syst., 43:1 – 55.
11 Lee, M.-C., Zhu, Q., Mavromatis, C., Han, Z., Adeshina, S., Ioannidis, V. N., Rangwala, H., and Faloutsos, C. (2025). Hybgrag: Hybrid retrieval-augmented generation on textual and relational knowledge bases. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), page 879–893. Association for Computational Linguistics.
12 Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In NIPS’2020, volume 33.
13 Matsumoto, N., Moran, J., Choi, H., Hernandez, M. E., Venkatesan, M., Wang, P., and Moore, J. H. (2024). Kragen: a knowledge graph-enhanced rag framework for biomedical problem solving using large language models. Bioinformatics, 40(6):btae353.
14 Min, C., Bansal, S., Pan, J., Keshavarzi, A., Mathew, R., and Kannan, A. V. (2025). Towards practical graphrag: Efficient knowledge graph construction and hybrid retrieval at scale. arXiv preprint arXiv:2507.03226.
15 Munikoti, S., Acharya, A., Wagle, S., and Horawalavithana, S. (2023). Atlantic: Structure-aware retrieval-augmented language model for interdisciplinary science. arXiv preprint arXiv:2311.12289.
16 Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., and Tang, S. (2024). Graph retrieval-augmented generation: A survey. ACM Trans. Inf. Syst.
17 Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E. (2023). A survey on multimodal large language models. Natl. Sci. Rev., 11.
18 Yu, H. Q. and McQuade, F. (2025). RAG-KG-IL: A multi-agent hybrid framework for reducing hallucinations and enhancing llm reasoning through rag and incremental knowledge graph learning integration. arXiv preprint arXiv:2503.13514.
19 Zhang, B. and Soh, H. (2024). Extract, define, canonicalize: An llm-based framework for knowledge graph construction. In Proceedings of the 2024 conference on empirical methods in natural language processing, pages 9820–9836.
20 Zhang, Q., Chen, S., Bei, Y.-Q., Yuan, Z., Zhou, H., Hong, Z., Dong, J., Chen, H., Chang, Y., and Huang, X. (2025). A survey of graph retrieval-augmented generation for customized large language models. ArXiv, abs/2501.13958.
21 Zhuang, L., Chen, S., Xiao, Y., Zhou, H., Zhang, Y., Chen, H., Zhang, Q., and Huang, X (2025). Linearrag: Linear graph retrieval augmented generation on large-scale corpora. Arxiv, abs/2510.10114.