The new wave of Large Language Models (LLM) has offered an efficient tool to curate sizeable conversational datasets. So far studies have mainly focused on task-oriented or generic open-domain dialogs, and have not fully explored the ability of LLMs in following complicated prompts. In this work, we focus on personalization, and employ LLMs to curate a dataset which is difficult and costly to crowd-source: PersonalityChat is a synthetic conversational dataset based upon the popular PersonaChat dataset, but conditioned on both personas and (Big-5) personality traits. Evaluating models fine-tuned on this dataset, we show that the personality trait labels can be used for trait-based personalization of generative dialogue models. We also perform a head-to-head comparison between PersonalityChat and PersonaChat, and show that training on the distilled dataset results in more fluent and coherent dialog agents in the small-model regime.
Augmenting Dialog with Think-Aloud Utterances for Modeling Individual Personality Traits by LLM
Institute of Science Tokyo（东京科学大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Fujitsu Limited（富士通株式会社）
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）
A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism
Department of Computer Science（计算机科学系）；University at Albany（阿尔巴尼大学）；Department of Management Information System（管理信息系统系）；West Virginia University（西弗吉尼亚大学）；Department of Radiology（放射学系）；Washington University in St. Louis（圣路易斯华盛顿大学）；Humanities and Social Sciences（人文学与社会科学）
From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
Department of Computer Science（计算机科学系）；Information Engineering, National Cheng Kung University, Taiwan（信息工程系，国立云林科技大学，台湾）；Department of Computer Science, National Tsing Hua University, Taiwan（计算机科学系，国立清华大学，台湾）
Samsung R&D Institute of China - Beijing (SRC-B)（三星中国研发研究院-北京（SRC-B））；Utrecht University（乌得勒支大学）；Tsinghua University（清华大学）
Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of spea…
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction
Tongji University（同济大学）；Tongyi Laboratory（通义实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所信息处理实验室）；Alibaba Group（阿里巴巴集团）；University of Chinese Academy of Sciences（中国科学院大学）
Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles
SRIBD, School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong（香港中文大学（深圳）数据科学学院）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究院）；Department of ECE, National University of Singapore（新加坡国立大学电子工程系）
PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech Dialogs
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanjing University, Suzhou（南京大学（苏州））；National University of Singapore（新加坡国立大学）；School of Data Science（数据科学学院）；School of Intelligence Science and Technology（智能科学与技术学院）；Department of ECE（电子与计算机工程系）
The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
Department of Artificial Intelligence, Chung-Ang University, Seoul, 06974, Republic of Korea（Chung-Ang大学人工智能系）；Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada（不列颠哥伦比亚大学计算机科学系）；Van Lang University, Ho Chi Minh City, Vietnam（文-lang大学）
Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
School of Computer Science, University of Technology Sydney（技术科技大学计算机科学学院）；Australian Institute of Health Innovation, Macquarie University（麦觉里大学健康创新研究所）
Facebook AI Research（Facebook人工智能研究（Facebook AI Research））
Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters. The vast training data required to train these architectures aggregates many different styles, tones, and qualities. Using that data to train a single model makes it difficult to use the model as a consistent conversational agent, e.g. with a stable set of persona traits and a typical style of expression. Several architectures affording control mechanisms over generation architectures have been proposed, each with different trade-offs. …
Modeling Layered Consciousness with Multi-Agent Large Language Models
Samsung Electronics（三星电子）；Hanyang University（汉阳大学）；Miami University（迈阿密大学）；The Catholic University of Korea College of Medicine（韩国天主教大学医学院）；Artificial Consciousness Lab, MODULABS（MODULABS 人工意识实验室）
To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014). In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. To test such models, we collect a dataset of grounded human-human conversati…
Although people have the ability to engage in vapid dialogue without effort, this may not be a uniquely human trait. Since the 1960's researchers have been trying to create agents that can generate artificial conversation. These programs are commonly known as chatbots. With increasing use of neural networks for dialog generation, some conclude that this goal has been achieved. This research joins the quest by creating a dialog generating Recurrent Neural Network (RNN) and by enhancing the ability of this network with auxiliary loss functions and a beam search. Our custom loss functions achieve…
