Large Language Models (LLMs) can be conditioned with explicit personality prompts, yet their behavioral realization often varies depending on context. This study examines how identical personality prompts lead to distinct linguistic, behavioral, and emotional outcomes across four conversational settings: ice-breaking, negotiation, group decision, and empathy tasks. Results show that contextual cues systematically influence both personality expression and emotional tone, suggesting that the same traits are expressed differently depending on social and affective demands. This raises an important question for LLM-based dialogue agents: whether such variations reflect inconsistency or context-sensitive adaptation akin to human behavior. Viewed through the lens of Whole Trait Theory, these findings highlight that LLMs exhibit context-sensitive rather than fixed personality expression, adapting flexibly to social interaction goals and affective conditions.
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）
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）
Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models
College of Computer and Information Sciences, King Saud University（计算机与信息科学学院，沙特阿拉伯国王沙德大学）
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））
Generative Personality Simulation via Theory-Informed Structured Interview
Department of Psychological Sciences & 2 Department of Computer Science, Rice University（1 心理学系 & 2 计算机科学系，莱斯大学）；Department of Electrical and Computer Engineering, Northeastern University（3 电气与计算机工程系，东北大学）；Department of Computer Science, Johns Hopkins University（4 计算机科学系，约翰霍普金斯大学）；Microsoft Research Asia（微软亚洲研究院）
Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts
Information Sciences Institute, University of Southern California（南加州大学信息科学学院）；Iovine and Young Academy, University of Southern California（南加州大学约万与杨学院）；In Vivo Group
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；TUM University Hospital（慕尼黑技术大学医院）；Google DeepMind（谷歌DeepMind）；Google Research（谷歌研究）；Imperial College London（伦敦帝国学院）
HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection
School of Electronics and Computer Science University of Southampton（电子与计算机科学学院 英国南安普顿大学）；Department of Computational Biology Mohamed bin Zayed University of Artificial Intelligence（计算生物学系 摩洛哥本·扎耶德人工智能大学）
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Fudan University（复旦大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；Renmin University of China（中国人民大学）；Beijing University of Technology（北京工业大学）；Boston University（波士顿大学）；RhineAI（莱茵AI公司）；SenseTime（商汤科技）
Dynamic Personality Adaptation in Large Language Models via State Machines
Institute for Geoinformatics, University of Münster（地理信息研究所，穆尔斯特大学）；Faculty of Mathematics and Computer Science, University of Münster（数学与计算机科学学院，穆尔斯特大学）；Department of Psychology, University of Münster（心理学系，穆尔斯特大学）；Institute of Medical Education and Student Affairs, University of Münster（医学教育与学生事务研究所，穆尔斯特大学）
