  Hate speech is a severe issue that affects many online platforms. So far,
several studies have been performed to develop robust hate speech detection
systems. Large language models like ChatGPT have recently shown a great promise
in performing several tasks, including hate speech detection. However, it is
crucial to comprehend the limitations of these models to build robust hate
speech detection systems. To bridge this gap, our study aims to evaluate the
strengths and weaknesses of the ChatGPT model in detecting hate speech at a
granular level across 11 languages. Our evaluation employs a series of
functionality tests that reveals various intricate failures of the model which
the aggregate metrics like macro F1 or accuracy are not able to unfold. In
addition, we investigate the influence of complex emotions, such as the use of
emojis in hate speech, on the performance of the ChatGPT model. Our analysis
highlights the shortcomings of the generative models in detecting certain types
of hate speech and highlighting the need for further research and improvements
in the workings of these models.
