This study explores linguistic differences between human and LLM-generated dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the EmpathicDialogues dataset. The research employs Linguistic Inquiry and Word Count (LIWC) analysis, comparing ChatGPT-generated conversations with human conversations across 118 linguistic categories. Results show greater variability and authenticity in human dialogues, but ChatGPT excels in categories such as social processes, analytical style, cognition, attentional focus, and positive emotional tone, reinforcing recent findings of LLMs being "more human than human." However, no significant difference was found in positive or negative affect between ChatGPT and human dialogues. Classifier analysis of dialogue embeddings indicates implicit coding of the valence of affect despite no explicit mention of affect in the conversations. The research also contributes a novel, companion ChatGPT-generated dataset of conversations between two independent chatbots, which were designed to replicate a corpus of human conversations available for open access and used widely in AI research on language modeling. Our findings enhance understanding of ChatGPT's linguistic capabilities and inform ongoing efforts to distinguish between human and LLM-generated text, which is critical in detecting AI-generated fakes, misinformation, and disinformation.
Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT
Department of Computer Science（计算机科学系）；Department of Biomedical and Health Information Sciences（生物医学与健康信息科学系）；Department of Medicine（医学系）；Department of Communications（通讯系）
Is ChatGPT a Good Multi-Party Conversation Solver?
National Engineering Research Center of Speech and Language Information Processing（国家语音与语言信息处理工程技术研究中心）；University of Science and Technology of China（中国科学技术大学）
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
A blind spot for large language models: Supradiegetic linguistic information
University of Vermont（佛蒙特大学）；Vermont Advanced Computing Center（佛蒙特高级计算中心）；Vermont Complex Systems Center（佛蒙特复杂系统中心）；MassMutual Center of Excellence in Complex Systems and Data Science（MassMutual复杂系统与数据科学卓越中心）；Department of Computer Science, University of Vermont（佛蒙特大学计算机科学系）
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model
Carnegie Mellon University（卡内基梅隆大学）；LMU Munich（慕尼黑大学）；University of Oxford（牛津大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Allen Institute for AI（艾伦人工智能研究所）；IIT Delhi（德里印度理工学院）
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Soochow University（苏州大学）；University of Science and Technology（科技大学）
Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph
CeADAR - Ireland’s Centre for Applied AI, University College Dublin（爱尔兰应用人工智能中心CeADAR、都柏林大学学院）
What does ChatGPT return about human values? Exploring value bias in ChatGPT using a descriptive value theory
Institute D’Or for Research and Teaching（多尔研究与教学学院）；School of Psychology（心理学院）；School of Information Management（信息管理学院）；Dublin City University（都柏林城市大学）；University of Western Australia（西澳大学）
本研究基于 Schwartz 基础价值观理论，通过 OpenAI API 重复提示 ChatGPT 生成文本并使用词袋方法分析，发现其输出无明显价值偏见且高保真传递了价值内容，仅存在社会导向价值观的融合。
Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）
