  Large-scale language models, like ChatGPT, have garnered significant media
attention and stunned the public with their remarkable capacity for generating
coherent text from short natural language prompts. In this paper, we aim to
conduct a systematic inspection of ChatGPT's performance in two controllable
generation tasks, with respect to ChatGPT's ability to adapt its output to
different target audiences (expert vs. layman) and writing styles (formal vs.
informal). Additionally, we evaluate the faithfulness of the generated text,
and compare the model's performance with human-authored texts. Our findings
indicate that the stylistic variations produced by humans are considerably
larger than those demonstrated by ChatGPT, and the generated texts diverge from
human samples in several characteristics, such as the distribution of word
types. Moreover, we observe that ChatGPT sometimes incorporates factual errors
or hallucinations when adapting the text to suit a specific style.
