Evaluating the Performance of ChatGPT for Spam Email Detection

TL;DR

This study evaluates ChatGPT's effectiveness in spam email detection across English and Chinese datasets, comparing it with traditional machine learning models, and analyzes how prompt demonstrations influence its performance.

Contribution

It provides a comprehensive assessment of ChatGPT's capabilities for spam detection in multiple languages and explores the impact of in-context learning variations.

Findings

ChatGPT performs worse than deep learning models on large English datasets.

ChatGPT outperforms traditional models on low-resource Chinese datasets.

The number of demonstrations in prompts affects ChatGPT's spam detection accuracy.

Abstract

Email continues to be a pivotal and extensively utilized communication medium within professional and commercial domains. Nonetheless, the prevalence of spam emails poses a significant challenge for users, disrupting their daily routines and diminishing productivity. Consequently, accurately identifying and filtering spam based on content has become crucial for cybersecurity. Recent advancements in natural language processing, particularly with large language models like ChatGPT, have shown remarkable performance in tasks such as question answering and text generation. However, its potential in spam identification remains underexplored. To fill in the gap, this study attempts to evaluate ChatGPT's capabilities for spam identification in both English and Chinese email datasets. We employ ChatGPT for spam email detection using in-context learning, which requires a prompt instruction with (or without) a few demonstrations. We also investigate how the number of demonstrations in the prompt affects the performance of ChatGPT. For comparison, we also implement five popular benchmark methods, including naive Bayes, support vector machines (SVM), logistic regression (LR), feedforward dense neural networks (DNN), and BERT classifiers. Through extensive experiments, the performance of ChatGPT is significantly worse than deep supervised learning methods in the large English dataset, while it presents superior performance on the low-resourced Chinese dataset. This study provides insights into the potential and limitations of ChatGPT for spam identification, highlighting its potential as a viable solution for resource-constrained language domains.