Can LLMs Infer Personality from Real World Conversations?

• URL: http://arxiv.org/abs/2507.14355v1
• Date: Fri, 18 Jul 2025 20:22:47 GMT
• Title: Can LLMs Infer Personality from Real World Conversations?
• Authors: Jianfeng Zhu, Ruoming Jin, Karin G. Coifman,
• Abstract summary: Large Language Models (LLMs) offer a promising approach for scalable personality assessment from open-ended language.<n>Three state-of-the-art LLMs were tested using zero-shot prompting for BFI-10 item prediction and both zero-shot and chain-of-thought prompting for Big Five trait inference.<n>All models showed high test-retest reliability, but construct validity was limited.
• Score: 5.705775078773656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) such as OpenAI's GPT-4 and Meta's LLaMA offer a promising approach for scalable personality assessment from open-ended language. However, inferring personality traits remains challenging, and earlier work often relied on synthetic data or social media text lacking psychometric validity. We introduce a real-world benchmark of 555 semi-structured interviews with BFI-10 self-report scores for evaluating LLM-based personality inference. Three state-of-the-art LLMs (GPT-4.1 Mini, Meta-LLaMA, and DeepSeek) were tested using zero-shot prompting for BFI-10 item prediction and both zero-shot and chain-of-thought prompting for Big Five trait inference. All models showed high test-retest reliability, but construct validity was limited: correlations with ground-truth scores were weak (max Pearson's $r = 0.27$), interrater agreement was low (Cohen's $\kappa < 0.10$), and predictions were biased toward moderate or high trait levels. Chain-of-thought prompting and longer input context modestly improved distributional alignment, but not trait-level accuracy. These results underscore limitations in current LLM-based personality inference and highlight the need for evidence-based development for psychological applications.

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