This paper investigates the application of pre-trained large language models
(LLMs) for spam email classification using zero-shot prompting. We evaluate the
performance of both open-source (Flan-T5) and proprietary LLMs (ChatGPT, GPT-4)
on the well-known SpamAssassin dataset. Two classification approaches are
explored: (1) truncated raw content from email subject and body, and (2)
classification based on summaries generated by ChatGPT. Our empirical analysis,
leveraging the entire dataset for evaluation without further training, reveals
promising results. Flan-T5 achieves a 90% F1-score on the truncated content
approach, while GPT-4 reaches a 95% F1-score using summaries. While these
initial findings on a single dataset suggest the potential for classification
pipelines of LLM-based subtasks (e.g., summarisation and classification),
further validation on diverse datasets is necessary. The high operational costs
of proprietary models, coupled with the general inference costs of LLMs, could
significantly hinder real-world deployment for spam filtering.

该研究探讨了使用零 - shot 提示的预训练大语言模型（LLMs）在垃圾邮件分类中的应用。通过对著名的 SpamAssassin 数据集进行评估，我们评估了开源模型（Flan-T5）和专有模型（ChatGPT，GPT-4）的性能。我们探讨了两种分类方法：（1）仅使用邮件主题和正文的截断原始内容，（2）基于 ChatGPT 生成的摘要进行分类。基于整个数据集的经验分析结果表明，Flan-T5 在截断内容方法上的 F1 得分为 90％，而 GPT-4 使用摘要则达到 95％的 F1 得分。尽管这些初步发现表明 LLMs 子任务（例如摘要和分类）的分类管道具有潜力，但仍需要在各种数据集上进行进一步验证。专有模型的高运营成本，以及 LLMs 的一般推断成本，可能会显著阻碍垃圾邮件过滤的实际部署。