Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection

Understand

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.