We present a novel end-to-end personality-based synthetic dialogue data generation pipeline, specifically designed to elicit responses from large language models via prompting. We design the prompts to generate more human-like dialogues considering real-world scenarios when users engage with chatbots. We introduce PSYDIAL, the first Korean dialogue dataset focused on personality-based dialogues, curated using our proposed pipeline. Notably, we focus on the Extraversion dimension of the Big Five personality model in our research. Experimental results indicate that while pre-trained models and those fine-tuned with a chit-chat dataset struggle to generate responses reflecting personality, models trained with PSYDIAL show significant improvements. The versatility of our pipeline extends beyond dialogue tasks, offering potential for other non-dialogue related applications. This research opens doors for more nuanced, personality-driven conversational AI in Korean and potentially other languages. Our code is publicly available at https://github.com/jiSilverH/psydial.
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）
Prompt-based or in-context learning has achieved high zero-shot performance on many natural language generation (NLG) tasks. Here we explore the performance of prompt-based learning for simultaneously controlling the personality and the semantic accuracy of an NLG for task-oriented dialogue. We experiment with prompt-based learning on the PERSONAGE restaurant recommendation corpus to generate semantically and stylistically-controlled text for 5 different Big-5 personality types: agreeable, disagreeable, conscientious, unconscientious, and extravert. We test two different classes of discrete pr…
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…
Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona, by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained…
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…
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）
Renmin University of China（中国人民大学）；University of Montreal（蒙特利尔大学）
Personalized dialogue systems explore the problem of generating responses that are consistent with the user's personality, which has raised much attention in recent years. Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation. Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user's personality. Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personal…
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 …
We present a novel approach to generating scripts by using agents with different personality types. To manage character interaction in the script, we employ simulated dramatic networks. Automatic and human evaluation on multiple criteria shows that our approach outperforms a vanilla-GPT2-based baseline. We further introduce a new metric to evaluate dialogue consistency based on natural language inference and demonstrate its validity.
University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；U.S. Army Research Laboratory（美国陆军研究实验室）
Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a sta…
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…
