Evaluation of Transformer-Based Large Language Models for Email Spam Detection Using BERT, Phi, and Gemma
Carregando...
Fontes externas
Fontes externas
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
International Academic Press
Tipo
Artigo
Direito de acesso
Acesso aberto
Fontes externas
Fontes externas
Resumo
In this paper, we study how LLMs based on the transformer architecture work and the possibility of adjusting these models to use only the body of email messages to classify them as spam or ham. The models studied are BERT, Gemma, and Phi. All of them underwent quantization stages, fine-tuning with a real dataset, and evaluation with metrics commonly used in binary classification problems. The Gemma model achieves over 99% accuracy in detecting spam, standing out as the best among the compared models.
Descrição
Palavras-chave
Idioma
Inglês
Citação
Statistics, Optimization and Information Computing, v. 13, n. 2, p. 459-473, 2025.
