This paper investigates the effectiveness of large language models (LLMs) in
email spam detection by comparing prominent models from three distinct
families: BERT-like, Sentence Transformers, and Seq2Seq. Additionally, we
examine well-established machine learning techniques for spam detection, such
as Na\"ive Bayes and LightGBM, as baseline methods. We assess the performance
of these models across four public datasets, utilizing different numbers of
training samples (full training set and few-shot settings). Our findings reveal
that, in the majority of cases, LLMs surpass the performance of the popular
baseline techniques, particularly in few-shot scenarios. This adaptability
renders LLMs uniquely suited to spam detection tasks, where labeled samples are
limited in number and models require frequent updates. Additionally, we
introduce Spam-T5, a Flan-T5 model that has been specifically adapted and
fine-tuned for the purpose of detecting email spam. Our results demonstrate
that Spam-T5 surpasses baseline models and other LLMs in the majority of
scenarios, particularly when there are a limited number of training samples
available. Our code is publicly available at
https://github.com/jpmorganchase/emailspamdetection