Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection
This work addresses the problem of robust hate speech detection for online platforms by identifying limitations in generative models like ChatGPT, but it is incremental as it focuses on evaluation rather than proposing new solutions.
The study evaluated ChatGPT's performance in detecting hate speech across 11 languages and with emojis, revealing granular failures that aggregate metrics like macro F1 or accuracy miss, highlighting the model's shortcomings in certain types of hate speech detection.
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.