Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their capacity to project nuanced personalities and adopt diverse metaphorical roles raises a design question: how should an agent's persona and personality be calibrated to the moment? Recent evidence suggests that (i) moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks, and (ii) context-appropriate metaphors outperform static one-note assistants on user experience and uptake. Yet most CAs still fix both persona and style, risking misalignment when dynamics, urgency, and formality vary, for example in medical information seeking, fitness coaching, and reflective learning. We propose a Fluid Personality Framework that jointly adapts (1) the agent's metaphorical persona, such as coach, tutor, librarian, or tool, and (2) its personality expression intensity, low, medium, or high, as a function of task context, user goals and traits, and situational urgency. We sketch the framework and its core design dimensions.
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.
Elder-Sim: A Psychometrically Validated Platform for Personality-Stable Elderly Digital Twins
Yulin AI-Enhanced HealthCare Lab（云林人工智能增强医疗实验室）；School of Nursing, Fudan University（复旦大学护理学院）；School of Nursing, Medical College of Soochow University（苏州大学医学院护理学院）；School of Nursing, Dali University（大理大学护理学院）；Department of Advanced Interdisciplinary Studies, The University of Tokyo（东京大学先进跨学科研究部）；The First Affiliated Hospital of Soochow University（苏州大学第一附属医院）；Department of Nursing, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University（江西医学院护理部，南昌大学第一附属医院）；School of Social Development, East China University of Political Science and Law（中国政法大学社会发展学院）；Rory Meyers College of Nursing, New York University（纽约大学 Rory Meyers 护理学院）；New York University Shanghai（纽约大学上海校区）
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-t…
Queen Mary College, University of London（伦敦大学玛丽女王学院）；Constellation AI
We present an automated evaluation method to measure fluidity in conversational dialogue systems. The method combines various state of the art Natural Language tools into a classifier, and human ratings on these dialogues to train an automated judgment model. Our experiments show that the results are an improvement on existing metrics for measuring fluidity.
User ratings play a significant role in spoken dialogue systems. Typically, such ratings tend to be averaged across all users and then utilized as feedback to improve the system or personalize its behavior. While this method can be useful to understand broad, general issues with the system and its behavior, it does not take into account differences between users that affect their ratings. In this work, we conduct a study to better understand how people rate their interactions with conversational agents. One macro-level characteristic that has been shown to correlate with how people perceive th…
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Hong Kong Baptist University（香港浸会大学）；Huazhong University of Science and Technology（华中科技大学）
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；TUM University Hospital（慕尼黑技术大学医院）；Google DeepMind（谷歌DeepMind）；Google Research（谷歌研究）；Imperial College London（伦敦帝国学院）
Columbia University（哥伦比亚大学）；University of California, Davis（加州大学戴维斯分校）
Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stag…
There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantic…
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
School of Computer Science, Wuhan University（武汉大学计算机学院）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer and Cyber Security, Hebei Normal University（河北师范大学计算机与网络空间安全学院）
针对数学教学中学习者差异被忽视的问题，本文提出 PACE 个性化对话辅导智能体，结合学习风格画像、苏格拉底式反馈与个性化训练，提升教学适应性和学习效果。
Dialogue systems have become recently essential in our life. Their use is getting more and more fluid and easy throughout the time. This boils down to the improvements made in NLP and AI fields. In this paper, we try to provide an overview to the current state of the art of dialogue systems, their categories and the different approaches to build them. We end up with a discussion that compares all the techniques and analyzes the strengths and weaknesses of each. Finally, we present an opinion piece suggesting to orientate the research towards the standardization of dialogue systems building.
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Sheffield（谢菲尔德大学）；Queen Mary University of London（伦敦玛丽女王大学）
Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of these two conditions by modelling both verbal and non-verbal cues in a set of interviews. We propose a new approach of modelling short-term features with visibility-signature transform, and compare it with widely used high-level statistical functions. We demonstrate the superior performance of our proposed signature-based mo…
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（清华大学计算机科学与技术系、人工智能研究院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Tencent（腾讯）
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Zurich University of Applied Sciences（应用科学大学苏黎世）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Bielefeld University（比勒菲尔德大学）
Tencent（腾讯）；Peking University（北京大学）；University of Macau（澳门大学）；University of Chinese Academy of Sciences（中国科学院大学）
Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progresses achieved by recent studies in this field, persona information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the persona can be involved via the End-to-End learning. This paper proposes to adopt the personality-related characteristics of human conversations into variati…
ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations
INESC-ID；Instituto Superior Técnico, University of Lisbon（里斯本大学高级技术学院）；Faculdade de Letras, University of Lisbon（里斯本大学文学院）
Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）；Alibaba Group（阿里巴巴集团）
Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-…
Gunrock 2.0 is built on top of Gunrock with an emphasis on user adaptation. Gunrock 2.0 combines various neural natural language understanding modules, including named entity detection, linking, and dialog act prediction, to improve user understanding. Its dialog management is a hierarchical model that handles various topics, such as movies, music, and sports. The system-level dialog manager can handle question detection, acknowledgment, error handling, and additional functions, making downstream modules much easier to design and implement. The dialog manager also adapts its topic selection to…
In human conversational interactions, turn-taking exchanges can be coordinated using cues from multiple modalities. To design spoken dialog systems that can conduct fluid interactions it is desirable to incorporate cues from separate modalities into turn-taking models. We propose that there is an appropriate temporal granularity at which modalities should be modeled. We design a multiscale RNN architecture to model modalities at separate timescales in a continuous manner. Our results show that modeling linguistic and acoustic features at separate temporal rates can be beneficial for turn-takin…
