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）
This study proposes augmenting dialog data with think-aloud utterances (TAUs) for modeling individual personalities in text chat by LLM. TAU is a verbalization of a speaker's thought before articulating the utterance. We expect "persona LLMs" trained with TAU-augmented data can mimic the speaker's personality trait better. We tested whether the trained persona LLMs obtain the human personality with respect to Big Five, a framework characterizing human personality traits from five aspects. The results showed that LLMs trained with TAU-augmented data more closely align to the speakers' Agreeableness and Neuroticism of Big Five than those trained with original dialog data. We also found that the quality of TAU-augmentation impacts persona LLM's performance.
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）
Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues
Aptima, Inc.（Aptima公司）；Human Systems Engineering, Arizona State University（亚利桑那州立大学人类系统工程系）；Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
Dynamic Personality Adaptation in Large Language Models via State Machines
Institute for Geoinformatics, University of Münster（地理信息研究所，穆尔斯特大学）；Faculty of Mathematics and Computer Science, University of Münster（数学与计算机科学学院，穆尔斯特大学）；Department of Psychology, University of Münster（心理学系，穆尔斯特大学）；Institute of Medical Education and Student Affairs, University of Münster（医学教育与学生事务研究所，穆尔斯特大学）
University of Pittsburgh（匹兹堡大学）；Adapt Centre, Trinity College Dublin（都柏林圣三一学院适应中心）
Research on human spoken language has shown that speech plays an important role in identifying speaker personality traits. In this work, we propose an approach for identifying speaker personality traits using overlap dynamics in multiparty spoken dialogues. We first define a set of novel features representing the overlap dynamics of each speaker. We then investigate the impact of speaker personality traits on these features using ANOVA tests. We find that features of overlap dynamics significantly vary for speakers with different levels of both Extraversion and Conscientiousness. Finally, we f…
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）
Linguistic markers of personality traits have been studied extensively, but few cross-cultural studies exist. In this paper, we evaluate how native speakers of American English and Arabic perceive personality traits and naturalness of English utterances that vary along the dimensions of verbosity, hedging, lexical and syntactic alignment, and formality. The utterances are the turns within dialogue fragments that are presented as text transcripts to the workers of Amazon's Mechanical Turk. The results of the study suggest that all four dimensions can be used as linguistic markers of all persona…
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…
Harbin Institute of Technology（哈尔滨工业大学）；Tencent AI Lab（腾讯人工智能实验室）
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is thus introduced to tackle the personality-inconsistent problem by incorporating explicit persona text into dialogue generation models. Despite the success of existing persona-based models on generating human-like responses, their one-stage decoding framework can hardly avoid the generation of inconsistent persona words. In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechani…
To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked. This may lead to inconsistent emotional expressions and disinterest users. To tackle this issue, we propose to equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion tra…
In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. We test our approach on the CraigslistBargain dataset and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opp…
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
The Chinese University of Hong Kong（香港中文大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；National University of Singapore（新加坡国立大学）；Peng Cheng Laboratory（鹏城实验室）
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）
Institute for Logic, Language and Computation（逻辑、语言与计算研究所）；University of Amsterdam（阿姆斯特丹大学）
Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging. In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.
The generation of personalized dialogue is vital to natural and human-like conversation. Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/personality of the interlocutor. As it is impractical to obtain the persona/personality representations for every interlocutor, recent works have explored the possibility of generating personalized dialogue by finetuning the model with dialogue examples corresponding to a given persona instead. However, in real-world implementations, a sufficient number …
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）
University of Potsdam（波茨坦大学）；Volkswagen AG（大众汽车公司）
Fully data driven Chatbots for non-goal oriented dialogues are known to suffer from inconsistent behaviour across their turns, stemming from a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. One reason for this is the relative lack of labeled data from which personality consistency and fact usage could be learned together with dialogue behaviour. To address this, we introduce a new labeled dialogue dataset in the domain of movie discussions, where every dialogue is based on pre-specified facts and opinions. We thoroughly validate t…
