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Is ChatGPT a Good Personality Recognizer? A Preliminary Study

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arxiv 2307.03952 v3 pith:YDHBZ5CC submitted 2023-07-08 cs.CL

classification cs.CL
keywords chatgptpersonalitypromptingrecognitionabilitygiventaskstext
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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.

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Cited by 2 Pith papers

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  1. Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition

    cs.CL 2026-07 conditional novelty 6.0 of 10

    JAM discovers theory-invariant pseudo-facets via attention-pooled graph prototypical networks, Cross-Theory Harmonization, and LLM-as-a-Judge, improving cross-framework balanced accuracy on Essays and Kaggle datasets.

  2. Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits

    cs.CL 2024-12 reject novelty 6.0 of 10

    A new 286-sample MBTI benchmark with psychologist-guided soft labels claims to fix self-report errors and match population trait distributions, but its soft-label estimation is not fully described.

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