Integrating LLMs and Chatbots Technologies - A Case Study on Brazilian Transit Law
Resumo
This paper proposes a hybrid integration of technologies for the development of chatbots. The proposed integration incorporates a framework for developing rule-based chatbots and Large Language Models (LLMs). As a case study of the proposed integration, an application was developed focused on Brazilian traffic legislation, specifically Law No. 9.503 of Sept. 23, 1997, Chapter XV - Infractions. The study demonstrates the technical feasibility of the proposed integration and addresses related challenges. It also identifies future research opportunities, such as adapting the chatbot to different laws and enhancing accessibility. In general, the study shows the potential of combining chatbot development frameworks with LLM to create sophisticated personalized chatbots.
Palavras-chave:
Chatbots, Brazilian Law, Large Language Models
Referências
Abreu, D.R.d.O.M., Souza, E.M.d., Mathias, T.A.d.F.: Impacto do código de trânsito brasileiro e da lei seca na mortalidade por acidentes de trânsito. Cadernos de Saúde Pública 34(8), (2018).
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BRASIL: Lei nº 9.503, de 23 de setembro de 1997. (1997), [link]
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Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., et al.: Exploring the potential of large language models (llms) in learning on graphs. arXiv:2307.03393 (2023)
Fan, W., Zhao, Z., Li, J., Liu, Y., Mei, X., Wang, Y., Tang, J., Li, Q.: Recommender systems in the era of large language models (LLMs). arXiv:2307.02046 (2023)
Gallo, S., Malizia, A., Paternò, F.: Towards a chatbot for creating trigger-action rules based on chatgpt and rasa. Symposium on End-User Development (2023)
Henrique, C.R.d.C.: Custos dos acidentes de trânsito no brasil: Estimativa simplificada com base na atualização das pesquisas do ipea sobre custos de acidentes nos aglomerados urbanos e rodovias. Instituto de Pesquisa Econômica Aplicada (2020)
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., McHardy, R.: Challenges and applications of large language models. arXiv:2307.10169 (2023)
Kasneci, E., Seßler, K., Küchemann, et al.: Chatgpt for good? on opportunities and challenges of large language models for education. Learning and individual differences 103, 102274 (2023)
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.t., Rocktäschel, T., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems 33, 9459–9474 (2020)
Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P.: Lost in the middle: How language models use long contexts. arXiv:2307.03172 (2023)
McTear, M., Varghese Marokkie, S., Bi, Y.: A comparative study of chatbot response generation: Traditional approaches versus large language models. In: International Conference on Knowledge Science, Engineering and Management. pp. 70–79. Springer (2023)
Research, G.V.: Chatbot market size, share & trends, analysis report by application (customer services, branding & advertising), by type, by vertical, by region (north america, europe, asia pacific, south america), and segment forecasts, 2023 - 2030 (2021), [link]
Sharma, R.K., Joshi, M.: An analytical study and review of open source chatbot framework, Rasa. International Journal of Engineering Research 9(06) (2020)
Shawar, B., Atwell, E.: Chatbots: Are they really useful? LDV Forum 22, 29–49 (2007)
Soler Valadés, P.: Agent educatiu multiassignatura: disseny i implementació amb rasa i docker. Tech. rep., Universitat de Barcelona (2023)
de Sousa, L.H.H., Trajano, G., Morales, A.S., Sarkadi, S., Panisson, A.R.: Using chatbot technologies to support argumentation. In: 16th International Conference on Agents and Artificial Intelligence (ICAART 2024). SciTePress (2024)
Trad, A.: A Conversational Dialogue System for Nutrition And Search Queries. Master’s thesis, ULM University (2023)
Trajano, G., Engelmann, D.C., Bordini, R.H., Sarkadi, S., Mumford, J., Panisson, A.R.: Translating natural language arguments to computational arguments using llms. In: Computational Models of Argument. pp. 289–300. IOS Press (2024)
Zhao, R., Chen, H., Wang, W., Jiao, F., Do, X.L., Qin, C., Ding, B., Guo, X., Li, M., Li, X., et al.: Retrieving multimodal information for augmented generation: A survey. arXiv:2303.10868 (2023)
Bansal, H., Khan, R.: A review paper on human computer interaction. International Journal of Advanced Research in Computer Science and Software Engineering 8(53) (2018) 12 Rocho et al.
BRASIL: Lei nº 9.503, de 23 de setembro de 1997. (1997), [link]
BRASIL: Lei complementar nº 95, de 26 de fevereiro de 1998 (1998), [link]
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., et al.: Exploring the potential of large language models (llms) in learning on graphs. arXiv:2307.03393 (2023)
Fan, W., Zhao, Z., Li, J., Liu, Y., Mei, X., Wang, Y., Tang, J., Li, Q.: Recommender systems in the era of large language models (LLMs). arXiv:2307.02046 (2023)
Gallo, S., Malizia, A., Paternò, F.: Towards a chatbot for creating trigger-action rules based on chatgpt and rasa. Symposium on End-User Development (2023)
Henrique, C.R.d.C.: Custos dos acidentes de trânsito no brasil: Estimativa simplificada com base na atualização das pesquisas do ipea sobre custos de acidentes nos aglomerados urbanos e rodovias. Instituto de Pesquisa Econômica Aplicada (2020)
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., McHardy, R.: Challenges and applications of large language models. arXiv:2307.10169 (2023)
Kasneci, E., Seßler, K., Küchemann, et al.: Chatgpt for good? on opportunities and challenges of large language models for education. Learning and individual differences 103, 102274 (2023)
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.t., Rocktäschel, T., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems 33, 9459–9474 (2020)
Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P.: Lost in the middle: How language models use long contexts. arXiv:2307.03172 (2023)
McTear, M., Varghese Marokkie, S., Bi, Y.: A comparative study of chatbot response generation: Traditional approaches versus large language models. In: International Conference on Knowledge Science, Engineering and Management. pp. 70–79. Springer (2023)
Research, G.V.: Chatbot market size, share & trends, analysis report by application (customer services, branding & advertising), by type, by vertical, by region (north america, europe, asia pacific, south america), and segment forecasts, 2023 - 2030 (2021), [link]
Sharma, R.K., Joshi, M.: An analytical study and review of open source chatbot framework, Rasa. International Journal of Engineering Research 9(06) (2020)
Shawar, B., Atwell, E.: Chatbots: Are they really useful? LDV Forum 22, 29–49 (2007)
Soler Valadés, P.: Agent educatiu multiassignatura: disseny i implementació amb rasa i docker. Tech. rep., Universitat de Barcelona (2023)
de Sousa, L.H.H., Trajano, G., Morales, A.S., Sarkadi, S., Panisson, A.R.: Using chatbot technologies to support argumentation. In: 16th International Conference on Agents and Artificial Intelligence (ICAART 2024). SciTePress (2024)
Trad, A.: A Conversational Dialogue System for Nutrition And Search Queries. Master’s thesis, ULM University (2023)
Trajano, G., Engelmann, D.C., Bordini, R.H., Sarkadi, S., Mumford, J., Panisson, A.R.: Translating natural language arguments to computational arguments using llms. In: Computational Models of Argument. pp. 289–300. IOS Press (2024)
Zhao, R., Chen, H., Wang, W., Jiao, F., Do, X.L., Qin, C., Ding, B., Guo, X., Li, M., Li, X., et al.: Retrieving multimodal information for augmented generation: A survey. arXiv:2303.10868 (2023)
Publicado
17/11/2024
Como Citar
ROCHO, Rafael Selau M.; PEREZ, Anderson Luiz Fernandes; FARIAS, Giovani P.; PANISSON, Alison R..
Integrating LLMs and Chatbots Technologies - A Case Study on Brazilian Transit Law. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 731-742.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245203.
