Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice Activity Projection (VAP), to improve conversational dynamics in HRI. These models are trained on human-human dialogue data using self-supervised learning objectives, without requiring domain-specific fine-tuning. We propose methods for using these models in tandem to predict when a robot should begin preparing responses, take turns, and handle potential interruptions. We evaluated the proposed system in a within-subject study against a traditional baseline system, using the Furhat robot with 39 adults in a conversational setting, in combination with a large language model for autonomous response generation. The results show that participants significantly prefer the proposed system, and it significantly reduces response delays and interruptions.
Japanese Stroke LLM Evaluation: A Conversational Benchmark for Safe Stroke Care in Japanese Using Large Language Models
National Hospital Organization Kagoshima Medical Center（国立医院机构鹿儿岛医疗中心）；Imamura General Hospital（今村综合医院）；Iwamoto Neurosurgery（岩本神经外科）；Kagoshima University（鹿儿岛大学）
Impact of Decoding Methods on Human Alignment of Conversational LLMs
Birla Institute of Technology and Science, Pilani（比拉理工与科学学院（皮拉尼））；National University of Singapore（新加坡国立大学）；NUS Center for Trusted Internet and Community（新加坡国立大学可信互联网与社区中心）；Department of Communications and New Media, National University of Singapore（新加坡国立大学传播与新媒体系）
本文研究了Beam Search、Top K Sampling和Nucleus Sampling等解码方法对LLM与人类对话对齐的影响，提出了内容、风格和心理测量导向的新衡量标准，发现较少beam数和较低P值对齐效果更好，且任务导向与开放式数据集表现不同。
Conversational Complexity for Assessing Risk in Large Language Models
University of Cambridge（剑桥大学）；Spanish National Research Council（西班牙国家研究委员会）；Universitat Politècnica de València（瓦伦西亚理工大学）；Leverhulme Centre for the Future of Intelligence（勒沃休姆未来智能中心）；Center for Automation and Robotics（自动化与机器人中心）；Valencian Research Institute for Artificial Intelligence（瓦伦西亚人工智能研究所）
Role-aware Heuristic Episodic Attention for Conversational LLMs
National Key Laboratory of Parallel and Distributed Computing（平行与分布式计算国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；National University of Defense Technology（国防科学技术大学）
OncoGPT: A Medical Conversational Model Tailored with Oncology Domain Expertise on a Large Language Model Meta-AI (LLaMA)
Shenzhen Kanghua Juntai Biotech Co. Ltd.（深圳康华君泰生物科技有限公司）；University of California, Davis（加利福尼亚大学戴维斯分校）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES)
Division of Speech, Music and Hearing（语音、音乐与听觉研究室）；KTH Royal Institute of Technology（皇家理工学院）；Department of Computer Science and Engineering（计算机科学与工程系）；Chalmers University of Technology（查尔姆斯理工大学）；Gothenburg University（哥德堡大学）；Department of Philosophy, Linguistics, Theory of Science（哲学、语言学与科学理论系）；University of Gothenburg（哥德堡大学）；Division of Health Informatics and Logistics（健康信息学与物流研究室）
Towards Stable and Personalised Profiles for Lexical Alignment in Spoken Human-Agent Dialogue
Centre for Language Studies, Centre for Language and Speech Technology, Radboud University,Nijmegen, The Netherlands（语言研究所以及语言与语音技术中心，拉德堡德大学，尼姆egen，荷兰）；Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands（行为科学研究所，拉德堡德大学，尼姆egen，荷兰）
Towards Human-centered Proactive Conversational Agents
National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Georgetown University（乔治城大学）
MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；National Taiwan University（国立台湾大学）；Massachusetts Institute of Technology（麻省理工学院）；Carnegie Mellon University（卡内基梅隆大学）；Meta AI
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
Zurich University of Applied Sciences (ZHAW)（苏黎世应用科技大学）；University of the Basque Country (UPV/EHU)（巴斯克地区大学）；National Distance Education University (UNED)（国家远程教育大学）；Synapse Développement（Synapse Développement（Synapse 发展公司））
缺乏高效且可靠的评估方法阻碍了会话式对话系统（聊天机器人）的发展。需要人类与聊天机器人对话的评估是时间和成本密集型的，对人类法官提出了很高的认知要求，并产生低质量的结果。在这项工作中，我们介绍了\emph{Spot The Bot}，一个具有成本效益和强大的评估框架，取代人与机器人之间的对话对话。然后，人类法官只为对话中的每个实体注释他们是否认为它是人类（假设这些对话中有人类参与者）。然后，这些注释允许我们根据聊天机器人模仿人类对话行为的能力对它们进行排名。由于我们预计所有机器人最终都会被认可，因此我们引入了一个指标来衡量哪些聊天机器人可以最长时间地维持类似人类的行为，即，\emph{Survival Analysis}。该指标能够将机器人的性能与其某些特性（例如，\流畅或敏感），产生可解释的结果。我们框架的低成本允许在评估周期内频繁评估聊天机器人。我们通过将\emph{Spot The Bot}应用于三个领域，评估几个最先进的聊天机器人，并与相关工作进行比较，来实证我们的主张。该框架作为一个即用型工具发布。
