Is ChatGPT the ultimate Data Augmentation Algorithm?

In the aftermath of GPT-3.5, commonly known as ChatGPT, research has attempted to assess its capacity for lowering annotation cost, either by doing zero-shot learning, generating new data, or replacing human annotators. Some studies have also investigated its use for data augmentation (DA), but only in limited contexts, which still leaves the question of how ChatGPT performs compared to state-of-theart algorithms. In this paper, we use Chat-GPT to create new data both with paraphrasing and with zero-shot generation, and compare it to seven other algorithms. We show that while ChatGPT performs exceptionally well on some datasets, it overall does not perform better than the other algorithms, yet demands a much larger implication from the practitioner due to ChatGPT often refusing to answer due to sensitive content in the datasets.