Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use the progress agents make towards the goal as a reinforcement learning reward signal to directly inform not only navigation actions, but also both question and answer generation. We demonstrate that RMM enables better generalization to novel environments. Interlocutor modelling may be a way forward for human-agent dialogue where robots need to both ask and answer questions.
University of California, Los Angeles（加州大学洛杉矶分校）；Columbia University（哥伦比亚大学）
Building a socially intelligent agent involves many challenges. One of which is to track the agent's mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent's mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empir…
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation
Department of Electrical Engineering and Computer Science, Indian Institute of Science Education and Research Bhopal（电子工程与计算机科学系，比哈尔印度科学教育与研究中心）
It may be difficult for some individuals to open up and share their thoughts and feelings in front of a mental health expert. For those who are more at ease with a virtual agent, conversational agents can serve as an intermediate step in the right direction. The conversational agent must therefore be empathetic and able to conduct free-flowing conversations. To this effect, we present an approach for creating a generative empathetic open-domain chatbot that can be used for mental health applications. We leverage large scale pre-training and empathetic conversational data to make the responses …
Universite de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Université de Toulouse（图卢兹大学）
Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction. We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling. We experimen…
Confusion is a mental state triggered by cognitive disequilibrium that can occur in many types of task-oriented interaction, including Human-Robot Interaction (HRI). People may become confused while interacting with robots due to communicative or even task-centred challenges. To build a smooth and engaging HRI, it is insufficient for an agent to simply detect confusion; instead, the system should aim to mitigate the situation. In light of this, in this paper, we present our approach to a linguistic design of dialogue policies to build a dialogue framework to alleviate interlocutor confusion. W…
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety
Department of Electrical & Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；Department of Computer Science, Princeton University（普林斯顿大学计算机科学系）；Department of Computer Science & Engineering, University of Michigan（密歇根大学计算机科学与工程系）；Department of Data Science & Engineering, University of Michigan（密歇根大学数据科学与工程系）；Department of Philosophy, Columbia University（哥伦比亚大学哲学系）；AI Lab, Princeton University（普林斯顿大学人工智能实验室）；Chen Frontier Lab for Al and Mental Health, Tianqiao and Chrissy Chen Institute（天桥及克里斯西·陈研究所人工智能与心理健康前沿实验室）；Theta Health Inc.（Theta健康公司）
MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialogue
Sichuan University（四川大学）；Shanghai Jiao Tong University（上海交通大学）；Mental Health Center, West China Hospital, Sichuan University（四川大学西昌医院心理健康中心）；WestChina School of Nursing, Sichuan University（四川大学西昌护理学院）；National University of Singapore（新加坡国立大学）
InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
Department of Mathematics, IIT Delhi（印度德里理工学院数学系）；Department of Electrical Engineering, IIT Delhi（印度德里理工学院电气工程系）；Department of Computer Science and Engineering, IIIT Manipur（曼尼普尔理工学院计算机科学与工程系）
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation
School of Computer Science and Engineering, Northeastern University（计算机科学与工程学院，东北大学）；Mental Health Education Center, Northeastern University（心理健康教育中心，东北大学）；School of Sociology and Psychology, Central University of Finance and Economics（社会学与心理学学院，中央财经大学）；School of Psychology, Northeast Normal University（心理学学院，东北师范大学）
LingxiDiagBench: A Multi-Agent Framework for Benchmarking LLMs in Chinese Psychiatric Consultation and Diagnosis
Tianqiao and Chrissy Chen Institute（天桥和克里斯西·陈研究所）；EverMind AI Inc.（EverMind AI公司）；Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海精神卫生中心，上海交通大学医学院）
