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.

翻译：本文比较了三种热门大型语言模型 (BERT，Sentence Transformers 和Seq2Seq) 和传统的机器学习方法，如朴素贝叶斯和LightGBM，在垃圾邮件检测方面的表现。我们使用不同大小的训练集，在四个公开数据集上评估这些模型的性能，并获得以下发现：在大多数情况下，大型语言模型的性能优于常见的基准方法，尤其是在小样本场景下。这种适应性使大型语言模型特别适合垃圾邮件检测任务，其中样本数量有限且模型需要频繁更新。此外，我们介绍了 Spam-T5，一种特别适用于电子邮件垃圾邮件检测的 Flan-T5 模型。我们的结果表明，Spam-T5 能在大多数场景下超越基准模型和其他大型语言模型，特别是在训练样本数量有限的情况下。我们的代码已公开发布在 https://github.com/jpmorganchase/emailspamdetection 上。