This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with persona-based inputs aligned to the Big Five personality traits: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Our approach guides LLMs to transform generic debunking content into personalized versions tailored to specific personality profiles. To assess the effectiveness of these transformations, we employ a separate LLM as an automated evaluator simulating corresponding personality traits, thereby eliminating the need for costly human evaluation panels. Our results show that personalized messages are generally seen as more persuasive than generic ones. We also find that traits like Openness tend to increase persuadability, while Neuroticism can lower it. Differences between LLM evaluators suggest that using multiple models provides a clearer picture. Overall, this work demonstrates a practical way to create more targeted debunking messages exploiting LLMs, while also raising important ethical questions about how such technology might be used.
Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology（湖北省智能信息处理与实时工业系统重点实验室）；Innovation, Policy and Entrepreneurship Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）创新、政策与创业方向）；Tübingen AI Center, University of Tübingen（图宾根大学图宾根人工智能中心）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）
Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models
Laboratoire de traitement et transport de l'information, Université Sorbonne Paris Nord, France（信息处理与传输实验室，索邦巴黎北大学，法国）；Institute of Communication Studies, National Yang Ming Chiao Tung University, Taiwan（传播学研究所，国立阳明交通大学，台湾）
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）
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））
Machine Mindset: An MBTI Exploration of Large Language Models
Panda Villa Tech（熊猫别墅科技）；Peking University（北京大学）；Macao Polytechnic University（澳门理工大学）；Huazhong University of Science and Technology（华中科技大学）；PengCheng Laboratory（鹏城实验室）
ELLIS Alicante（阿利坎特ELLIS）；University of South Bohemia（南波希米亚大学）；Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague（布拉格捷克理工大学捷克信息学、机器人与控制论研究所）
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
School of Computer Science, Wuhan University（武汉大学计算机学院）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer and Cyber Security, Hebei Normal University（河北师范大学计算机与网络空间安全学院）
针对数学教学中学习者差异被忽视的问题，本文提出 PACE 个性化对话辅导智能体，结合学习风格画像、苏格拉底式反馈与个性化训练，提升教学适应性和学习效果。
VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos
School of Information Science, Guangdong University of Finance and Economics（广东财经大学信息学院）；Guangdong Intelligent Business Engineering Technology Center（广东省智能商务工程技术中心）；Key Laboratory of Collaborative Innovation in Digital Economy（数字经济协同创新重点实验室）；School of Computer Science, Shandong University（山东大学计算机学院）
