In the realm of mimicking human deliberation, large language models (LLMs) show promising performance, thereby amplifying the importance of this research area. Deliberation is influenced by both logic and personality. However, previous studies predominantly focused on the logic of LLMs, neglecting the exploration of personality aspects. In this work, we introduce Dynamic Personality Generation (DPG), a dynamic personality generation method based on Hypernetworks. Initially, we embed the Big Five personality theory into GPT-4 to form a personality assessment machine, enabling it to evaluate characters' personality traits from dialogues automatically. We propose a new metric to assess personality generation capability based on this evaluation method. Then, we use this personality assessment machine to evaluate dialogues in script data, resulting in a personality-dialogue dataset. Finally, we fine-tune DPG on the personality-dialogue dataset. Experiments prove that DPG's personality generation capability is stronger after fine-tuning on this dataset than traditional fine-tuning methods, surpassing prompt-based GPT-4.
Split Personality Training: Revealing Latent Knowledge Through Alternate Personalities
University of Bergen, Bergen, Norway（卑尔根大学）；Independent Researcher, London, UK（独立研究者）；University College London, London, UK（伦敦大学学院）；Independent Researcher, Berlin, Germany（独立研究者）；Independent Researcher（独立研究者）；Head of Research（研发负责人）
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）
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）
Exploring the Impact of Personality Traits on LLM Bias and Toxicity
University of Macau（澳门大学）；Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳高性能数据挖掘重点实验室，深圳先进技术研究院，中国科学院）；Nanyang Technological University（南洋理工大学）；Department of Chinese Language and Literature, University of Macau（中文语言文学系，澳门大学）
EMNLP: Educator-role Moral and Normative Large Language Models Profiling
College of Education, Zhejiang University of Technology（浙江工业大学教育学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Faculty of Education, East China Normal University（华东师范大学教育学院）；GuangHua Law School, ZheJiang University（浙江大学光华法学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation
PRISM AI（PRISM人工智能公司）；Harmony Intelligence（和谐智能公司）；Leap Laboratories（leap实验室）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；ETH AI Center, ETH Zurich（苏黎世联邦理工学院人工智能中心）
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）
Tsinghua University（清华大学）；Samsung Research China - Beijing (SRC-B)（三星中国研究院-北京（SRC-B））；NetEase Inc.（网易公司）
Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue cont…
Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model
Key Laboratory of Big Data and Intelligent Robot (South China University of Technology) Ministry of Education（大数据与智能机器人重点实验室（华南理工大学）教育部）；School of Software Engineering, South China University of Technology（软件学院（华南理工大学））；The Hong Kong Polytechnic University（香港理工大学）
ControlLM: Crafting Diverse Personalities for Language Models
The Laboratory of Cognition and Decision Intelligence for Complex Systems, IA, CAS（中国科学院自动化研究所复杂系统认知与决策智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Unisound（云知声）
Saratoga High School（萨拉托加高中）；SingularityNET Foundation（奇点网络基金会）；Aigents（Aigents公司）；Novosibirsk State University（新西伯利亚国立大学）
Many current artificial general intelligence (AGI) and natural language processing (NLP) architectures do not possess general conversational intelligence--that is, they either do not deal with language or are unable to convey knowledge in a form similar to the human language without manual, labor-intensive methods such as template-based customization. In this paper, we propose a new technique to automatically generate grammatically valid sentences using the Link Grammar database. This natural language generation method far outperforms current state-of-the-art baselines and may serve as the fin…
South China University of Technology（华南理工大学）；UBTECH Robotics Corp（优必选科技股份有限公司）；Tsinghua University（清华大学）
Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the proce…
