Source: Anais. Conference titles: Congresso da Sociedade Brasileira de Computação - CSBC. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, APRENDIZADO COMPUTACIONAL, GOVERNO ELETRÔNICO, PROTEÇÃO DE DADOS PESSOAIS
ABNT
MARCACINI, Ricardo Marcondes et al. LLM4Gov: a privacy-preserving approach to teacher-student fine-tuning of distilled LLMs for the public sector. 2025, Anais.. Porto Alegre: SBC, 2025. Disponível em: https://doi.org/10.5753/lasdigov.2025.9471. Acesso em: 05 set. 2025.APA
Marcacini, R. M., Valverde-Rebaza, J. C., Turine, M. A. S., Santos, B. N. dos, Levcovitz, S., & Rezende, S. O. (2025). LLM4Gov: a privacy-preserving approach to teacher-student fine-tuning of distilled LLMs for the public sector. In Anais. Porto Alegre: SBC. doi:10.5753/lasdigov.2025.9471NLM
Marcacini RM, Valverde-Rebaza JC, Turine MAS, Santos BN dos, Levcovitz S, Rezende SO. LLM4Gov: a privacy-preserving approach to teacher-student fine-tuning of distilled LLMs for the public sector [Internet]. Anais. 2025 ;[citado 2025 set. 05 ] Available from: https://doi.org/10.5753/lasdigov.2025.9471Vancouver
Marcacini RM, Valverde-Rebaza JC, Turine MAS, Santos BN dos, Levcovitz S, Rezende SO. LLM4Gov: a privacy-preserving approach to teacher-student fine-tuning of distilled LLMs for the public sector [Internet]. Anais. 2025 ;[citado 2025 set. 05 ] Available from: https://doi.org/10.5753/lasdigov.2025.9471