With the increasing deployment of large language models (LLMs) in affective agents and AI systems, maintaining a consistent and authentic LLM personality becomes critical for user trust and engagement. However, existing work overlooks a fundamental psychological consensus that personality traits are dynamic and context-dependent. To bridge this gap, we introduce PTCBENCH, a systematic benchmark designed to quantify the consistency of LLM personalities under controlled situational contexts. PTCBENCH subjects models to 12 distinct external conditions spanning diverse location contexts and life events, and rigorously assesses the personality using the NEO Five-Factor Inventory. Our study on 39,240 personality trait records reveals that certain external scenarios (e.g., "Unemployment") can trigger significant personality changes of LLMs, and even alter their reasoning capabilities. Overall, PTCBENCH establishes an extensible framework for evaluating personality consistency in realistic, evolving environments, offering actionable insights for developing robust and psychologically aligned AI systems.
Do Personality Traits Interfere? Geometric Limitations of Steering in Large Language Models
Network Analysis and Social Influence Modelling (NASIM) Lab（网络分析与社会影响建模实验室）；School of Physics Maths and Computing（物理数学计算学院）；The University of Western Australia（西澳大学）；School of Computing（计算学院）；Macquarie University（麦觉瑞大学）
Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西湖大学工程学院）；Westlake University（西湖大学）；School of Early Childhood Education, Hangzhou Polytechnic（杭州职业技术学院幼儿教育学院）
Evaluating Personality Traits in Large Language Models: Insights from Psychological Questionnaires
School of Physics, Mathematics and Computing（物理、数学与计算学院）；University of Western Australia（西澳大学）；School of Computing（计算机学院）；School of Psychological Sciences（心理科学学院）
Neuron-based Personality Trait Induction in Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学首都人工智能学院）；Tongyi Lab（通义实验室）；Institute of Statistics and Big Data, Renmin University of China（中国人民大学统计与大数据研究院）
The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）
Personality Vector: Modulating Personality of Large Language Models by Model Merging
Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University（人类-人工智能交互系，成均馆大学）；Department of Immersive Media Engineering, Sungkyunkwan University（沉浸式媒体工程系，成均馆大学）
Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traits
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Microsoft Research Asia（微软亚洲研究院）；North Carolina State University（北卡罗来纳州立大学）
PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
Stony Brook University（石溪大学）；New York University（纽约大学）；University of Melbourne（墨尔本大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Pennsylvania（宾夕法尼亚大学）；Stanford University（斯坦福大学）
