大语言模型（LLMs），如OpenAI的GPT-4和Meta的LLaMA，为基于开放式语言的可扩展人格评估提供了一种有前景的方法。然而，推断人格特质仍然具有挑战性，早期工作往往依赖合成数据或缺乏心理测量效度的社交媒体文本。我们引入了一个包含555次半结构化访谈的真实世界基准，并配有BFI-10自评分数，用于评估基于LLM的人格推断。三种最先进的大语言模型（GPT-4.1 Mini、Meta-LLaMA和DeepSeek）分别使用零样本提示进行BFI-10条目预测，并使用零样本和思维链提示进行大五人格特质推断。所有模型均表现出较高的重测信度，但构念效度有限：与真实分数的相关性较弱（最大Pearson's r = 0.27），评分者间一致性较低（Cohen's κ < 0.10），且预测偏向中等或较高特质水平。思维链提示和更长的输入上下文适度改善了分布对齐，但未提高特质层面的准确性。这些结果凸显了当前基于LLM的人格推断的局限性，并强调了在心理学应用中开展循证开发的必要性。
英文摘要：
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 $κ< 0.10$), 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.
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Hong Kong Baptist University（香港浸会大学）；Huazhong University of Science and Technology（华中科技大学）
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
