Contrato360: uma aplicação de perguntas e respostas usando modelos de linguagem, documentos e bancos de dados
Resumo
Apresentamos uma metodologia para desenvolver aplicações de perguntas e respostas (Q&A), validada no processo de apoio à gestão de contratos. Capturando informação de documentos de contratos em formato PDF e dados provenientes do sistema de apoio, em seguida estas informações são submetidas ao GPT-4. A relevância das respostas é melhorada pelas técnicas de Recuperação Aumentada (RAG), Text-to-SQL e Engenharia de Prompt, dispensando o retreinamento do modelo de linguagem. Ao longo do trabalho, observamos que a soma dessas técnicas aumenta a relevância das respostas. Destacamos o potencial dos Modelos de Linguagem de Grande Escala (LLMs) na construção de sistemas de informação onde a linguagem natural é a interface.
Palavras-chave:
Contratos, grandes modelos de linguagem, perguntas e respostas, engenharia de prompt, rag, text-to-sql
Referências
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.
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.
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.
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.
Giray, L. (2023). Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633.
Jeong, C. (2023). A study on the implementation of generative ai services using an enterprise data-based llm application architecture. arXiv preprint arXiv:2309.01105.
Li, H., Su, Y., Cai, D., Wang, Y., and Liu, L. (2022). A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110.
Liu, A., Hu, X., Wen, L., and Yu, P. S. (2023). A comprehensive evaluation of chatgpt’s zero-shot text-to-sql capability. arXiv preprint arXiv:2303.13547.
OpenAI (2023a). Chatgpt fine-tune description. [link]. Accessed: 2024-03-01.
OpenAI (2023b). Chatgpt prompt engineering. [link]. Accessed: 2024-04-01.
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.
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.
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.
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.
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.
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.
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.
Giray, L. (2023). Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633.
Jeong, C. (2023). A study on the implementation of generative ai services using an enterprise data-based llm application architecture. arXiv preprint arXiv:2309.01105.
Li, H., Su, Y., Cai, D., Wang, Y., and Liu, L. (2022). A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110.
Liu, A., Hu, X., Wen, L., and Yu, P. S. (2023). A comprehensive evaluation of chatgpt’s zero-shot text-to-sql capability. arXiv preprint arXiv:2303.13547.
OpenAI (2023a). Chatgpt fine-tune description. [link]. Accessed: 2024-03-01.
OpenAI (2023b). Chatgpt prompt engineering. [link]. Accessed: 2024-04-01.
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.
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.
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.
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.
Publicado
14/10/2024
Como Citar
MEDEIROS, Antony Seabra de; CAVALCANTE, Claudio; NEPOMUCENO, João; LAGO, Lucas; RUBERG, Nicolaas; LIFSCHITZ, Sérgio.
Contrato360: uma aplicação de perguntas e respostas usando modelos de linguagem, documentos e bancos de dados. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 155-166.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240871.