Reducing costs in large-scale classification: a hybrid BERT-LLM strategy (2025)
- Authors:
- USP affiliated authors: UTINO, MATHEUS YASUO RIBEIRO - ICMC ; GÔLO, MARCOS PAULO SILVA - ICMC ; SANTOS, MARCELA APARECIDA ANICETO DO - ICMC
- Unidade: ICMC
- DOI: 10.1109/SBAC-PADW69789.2025.00026
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; APRENDIZADO COMPUTACIONAL; MINERAÇÃO DE DADOS; SUSTENTABILIDADE
- Keywords: Large Language Models; BERT-based models; Hybrid approach; Cost reduction; Pre-filter module
- Agências de fomento:
- Language: Inglês
- Objetivos de Desenvolvimento Sustentável (ODS):
09. Indústria, inovação e infraestrutura
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2025
- Source:
- Título: Proceedings
- Conference titles: IEEE/SBC International Symposium on Computer Architecture and High Performance Computing Workshops - SBAC-PADW
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
MIRANDA, Augusto Cesar Dalal et al. Reducing costs in large-scale classification: a hybrid BERT-LLM strategy. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/SBAC-PADW69789.2025.00026. Acesso em: 20 mar. 2026. -
APA
Miranda, A. C. D., Utino, M. Y. R., Gôlo, M. P. S., Santos, M. A. A. dos, & Souza, M. C. de. (2025). Reducing costs in large-scale classification: a hybrid BERT-LLM strategy. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SBAC-PADW69789.2025.00026 -
NLM
Miranda ACD, Utino MYR, Gôlo MPS, Santos MAA dos, Souza MC de. Reducing costs in large-scale classification: a hybrid BERT-LLM strategy [Internet]. Proceedings. 2025 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1109/SBAC-PADW69789.2025.00026 -
Vancouver
Miranda ACD, Utino MYR, Gôlo MPS, Santos MAA dos, Souza MC de. Reducing costs in large-scale classification: a hybrid BERT-LLM strategy [Internet]. Proceedings. 2025 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1109/SBAC-PADW69789.2025.00026 - Efficient filtering with BERT embeddings for researcher-topic affinity prediction in HPC pipelines
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