  In recent years, personality has been regarded as a valuable personal factor
being incorporated into numerous tasks such as sentiment analysis and product
recommendation. This has led to widespread attention to text-based personality
recognition task, which aims to identify an individual's personality based on
given text. Considering that ChatGPT has recently exhibited remarkable
abilities on various natural language processing tasks, we provide a
preliminary evaluation of ChatGPT on text-based personality recognition task
for generating effective personality data. Concretely, we employ a variety of
prompting strategies to explore ChatGPT's ability in recognizing personality
from given text, especially the level-oriented prompting strategy we designed
for guiding ChatGPT in analyzing given text at a specified level. The
experimental results on two representative real-world datasets reveal that
ChatGPT with zero-shot chain-of-thought prompting exhibits impressive
personality recognition ability and is capable to provide natural language
explanations through text-based logical reasoning. Furthermore, by employing
the level-oriented prompting strategy to optimize zero-shot chain-of-thought
prompting, the performance gap between ChatGPT and corresponding
state-of-the-art model has been narrowed even more. However, we observe that
ChatGPT shows unfairness towards certain sensitive demographic attributes such
as gender and age. Additionally, we discover that eliciting the personality
recognition ability of ChatGPT helps improve its performance on
personality-related downstream tasks such as sentiment classification and
stress prediction.
