  Recently, Large Language Models like ChatGPT have demonstrated remarkable
proficiency in various Natural Language Processing tasks. Their application in
Requirements Engineering, especially in requirements classification, has gained
increasing interest. This paper reports an extensive empirical evaluation of
two ChatGPT models, specifically gpt-3.5-turbo, and gpt-4 in both zero-shot and
few-shot settings for requirements classification. The question arises as to
how these models compare to traditional classification methods, specifically
Support Vector Machine and Long Short-Term Memory. Based on five different
datasets, our results show that there is no single best technique for all types
of requirement classes. Interestingly, the few-shot setting has been found to
be beneficial primarily in scenarios where zero-shot results are significantly
low.
