Is ChatGPT a Good Personality Recognizer? A Preliminary Study

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. We compare the performance of ChatGPT on two representative real-world datasets with traditional neural network, fine-tuned RoBERTa, and corresponding state-of-the-art task-specific model. The experimental results show that ChatGPT with zero-shot chain-of-thought prompting exhibits impressive personality recognition ability. Triggered by zero-shot chain-of-thought prompting, ChatGPT outperforms fine-tuned RoBERTa on the two datasets and is capable to provide natural language explanations through text-based logical reasoning. Furthermore, relative to zero-shot chain-of-thought prompting, zero-shot level-oriented chain-of-thought prompting enhances the personality prediction ability of ChatGPT and reduces the performance gap between ChatGPT and corresponding state-of-the-art task-specific model. Besides, we also conduct experiments to observe the fairness of ChatGPT when identifying personality and discover that ChatGPT shows unfairness to some sensitive demographic attributes such as gender and age.