Human problem-solving is enriched by a diversity of styles and personality traits, yet the development of Large Language Models (LLMs) has largely prioritized uniform performance benchmarks that favour specific behavioural tendencies such as assertiveness. To investigate how diverse experiences shape machine personality and influence problem-solving, this study employs continued pre-training to expose models to domain-specific texts in an unsupervised manner, simulating the accumulation of experience. By adapting the Big Five framework via the Machine Personality Inventory (MPI), we quantify the personality traits of these model variants and analyse their relationship to linguistic style and reasoning behaviour. The findings reveal that model competence is bimodal, peaking at "Expressive Generalists" and "Suppressed Specialists," while identifying a "Suppression Advantage" where reduced social traits enhance complex reasoning performance. This study further establishes a causal link between training data linguistics, such as imperative frequency, and lexical diversity, providing a roadmap for "Personality Engineering".
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）
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
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）
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）
