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English Information

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Authors
# Name
1 Jorge Pavão(jorge.pavao@aluno.cefet-rj.br)
2 Diego Brandão(diego.brandao@cefet-rj.br)
3 Kele Belloze(kele.belloze@cefet-rj.br)

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Reference
# Reference
1 Anisuzzaman, D., Malins, J. G., Friedman, P. A., and Attia, Z. I. (2025). Fine-tuning large language models for specialized use cases. Mayo Clinic Proceedings: Digital Health, 3(1):100184.
2 Benzinho, J. et al. (2024). Llm based chatbot for farm-to-fork blockchain traceability platform. Applied Sciences, 14(19).
3 Berryman, J. and Ziegler, A. (2024). Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications. O’Reilly Media, 1st edition.
4 Huber, M. and Imhof, D. (2019). Machine learning with screens for detecting bid-rigging cartels. International Journal of Industrial Organization, 65:277–301.
5 Huber, M., Imhof, D., and Ishii, R. (2022). Transnational machine learning with screens for flagging bid-rigging cartels. Journal of the Royal Statistical Society. Series A: Statistics in Society, 185:1074–1114.
6 Imhof, D. and Wallimann, H. (2021). Detecting bid-rigging coalitions in different countries and auction formats. International Review of Law and Economics, 68.
7 Lima, M. C. (2021). Deep vacuity: Detecção e classificação automática de padrões com risco de conluio em dados públicos de licitações de obras. Master’s thesis, Universidade de Brasília.
8 OCDE (2021). Combate a carteis em licitações no brasil: Uma revisão das compras públicas federais. Organização para a Cooperação e Desenvolvimento Econômico (OCDE).
9 Rodríguez, M. G. et al. (2022). Collusion detection in public procurement auctions with machine learning algorithms. Automation in Construction, 133.
10 Scoralick, L., Brandao, D., and Belloze, K. (2024). Aprimoramento de modelos para detecção de conluios em licitações públicas brasileiras com variáveis estatísticas e modelos explicáveis. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados , pages 680–686, Porto Alegre, RS, Brasil. SBC.
11 Silveira, D. et al. (2022). Won’t get fooled again: A supervised machine learning approach for screening gasoline cartels. Energy Economics, 105
12 Silveira, D. et al. (2023). Who are you? cartel detection using unlabeled data. International Journal of Industrial Organization, 88.
13 Souza, R. V. F. D. (2023). Identificação automática de conluio em pregões do Comprasnet com aprendizado de maquina. Master’s thesis, Universidade de Brasília.
14 Velasco, R. et al. (2021). A decision support system for fraud detection in public procurement. International Transactions in Operational Research, 28:27–47.
15 Wallimann, H., Imhof, D., and Huber, M. (2023). A machine learning approach for flagging incomplete bid-rigging cartels. Computational Economics, 62:1669–1720.