# Is ChatGPT a Good Personality Recognizer? A Preliminary Study

arXiv: https://arxiv.org/abs/2307.03952 · HTML: https://mlsanity.com/paper/2307.03952/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 76 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023)
- [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://mlsanity.com/paper/2305.07609/) (2023)
- [Personalized Machine Translation: Preserving Original Author Traits](https://mlsanity.com/paper/1610.05461/) (2016)
- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)

## Built on by (most cited first, 26 of 26)

- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [DialogBench: Evaluating LLMs as Human-like Dialogue Systems](https://mlsanity.com/paper/2311.01677/) (2023)
- [A Wide Evaluation of ChatGPT on Affective Computing Tasks](https://mlsanity.com/paper/2308.13911/) (2023)
- [LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model](https://mlsanity.com/paper/2403.07581/) (2024)
- [Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models](https://mlsanity.com/paper/2401.07115/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [Neuron-based Personality Trait Induction in Large Language Models](https://mlsanity.com/paper/2410.12327/) (2024)
- [Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies](https://mlsanity.com/paper/2309.02045/) (2023)
- [Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation](https://mlsanity.com/paper/2404.02589/) (2024)
- [Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues](https://mlsanity.com/paper/2409.19723/) (2024)
- [PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models](https://mlsanity.com/paper/2404.00930/) (2024)
- [Can ChatGPT Read Who You Are?](https://mlsanity.com/paper/2312.16070/) (2023)
- [Emotions in the Loop: A Survey of Affective Computing for Emotional Support](https://mlsanity.com/paper/2505.01542/) (2025)
- [Speaker Verification in Agent-Generated Conversations](https://mlsanity.com/paper/2405.10150/) (2024)
- [Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models](https://mlsanity.com/paper/2512.06991/) (2025)
- [Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement](https://mlsanity.com/paper/2312.08642/) (2023)
- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)
- [Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition](https://mlsanity.com/paper/2604.08851/) (2026)
- [LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion](https://mlsanity.com/paper/2606.29900/) (2026)
- [PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech Dialogs](https://mlsanity.com/paper/2505.14356/) (2025)
- [Mind Reading or Misreading? LLMs on the Big Five Personality Test](https://mlsanity.com/paper/2511.23101/) (2025)
- [Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition](https://mlsanity.com/paper/2607.08374/) (2026)
- [When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs](https://mlsanity.com/paper/2608.25977/) (2026)
- [World Model-Guided Reinforcement Learning via Counterfactual User Engagement Simulation](https://mlsanity.com/paper/2609.01067/) (2026)
- [Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings](https://mlsanity.com/paper/2610.16050/) (2026)

## Abstract (arXiv)

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.