This study investigates the elicitation of empathy toward a third party through interaction with social agents. Participants engaged with either a physical robot or a voice-enabled chatbot, both driven by a large language model (LLM) programmed to exhibit either an empathetic tone or remain neutral. The interaction is focused on a fictional character, Katie Banks, who is in a challenging situation and in need of financial donations. The willingness to help Katie, measured by the number of hours participants were willing to volunteer, along with their perceptions of the agent, were assessed for 60 participants. Results indicate that neither robotic embodiment nor empathetic tone significantly influenced participants' willingness to volunteer. While the LLM effectively simulated human empathy, fostering genuine empathetic responses in participants proved challenging.
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai AI Laboratory（上海人工智能实验室）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Philips Research（飞利浦研究院）；University of Cagliari（卡利亚里大学）
Empathetic response from the therapist is key to the success of clinical psychotherapy, especially motivational interviewing. Previous work on computational modelling of empathy in motivational interviewing has focused on offline, session-level assessment of therapist empathy, where empathy captures all efforts that the therapist makes to understand the client's perspective and convey that understanding to the client. In this position paper, we propose a novel task of turn-level detection of client need for empathy. Concretely, we propose to leverage pre-trained language models and empathy-rel…
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
Sun Yat-Sen University（中山大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Generative adversarial imitation learning (GAIL) is a model-free algorithm that has been shown to provide strong results in imitating complex behaviors in high-dimensional environments. In this paper, we utilize the GAIL model for text generation to develop empathy-based context-aware conversational AI. Our model uses an expert trajectory of empathetic prompt-response dialogues which can accurately exhibit the correct empathetic emotion when generating a response. The Generator of the GAIL model uses the GPT-2 sequential pre-trained language model trained on 117 million parameters from 40 GB o…
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
National Technical University of Athens（雅典国家技术大学）；ATHENA R.C.（雅典娜研究中心）；Behavioral Signals
In this paper, we introduce EmpBot: an end-to-end empathetic chatbot. Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the implied feelings of the conversation partner and respond appropriately. To this end, we propose a method based on a transformer pretrained language model (T5). Specifically, during finetuning we propose to use three objectives: response language modeling, sentiment understanding, and empathy forcing. The first objective is crucial for generating relevant and coherent responses, while the next ones are significant for…
ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework
University of Shanghai for Science and Technology（上海理工大学）；East China Normal University（华东师范大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Macquarie University（麦考瑞大学）
EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels
Kyoto Institute of Technology, Japan（京都技术大学）；National Institute of Information and Communications Technology (NICT), Japan（信息通信技术国家研究所）；Kyoto University, Japan（京都大学）；Nara Institute of Science and Technology, Japan（奈良科学技术大学）
PRIDE: Privileged Information-enhanced Distillation for Empathetic Dialogue Generation
Anhui Robot Technology Standard Innovation Base, School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院安徽机器人技术标准创新基地）
FreezeEmpath: Efficient Training for Empathetic Spoken Chatbots with Frozen LLMs
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences（智能信息处理重点实验室，计算技术研究所，中国科学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
Empathy by Design: Aligning Large Language Models for Healthcare Dialogue
Department of Public Health Sciences, The University of Texas at El Paso, USA（公共卫生科学系，德克萨斯理工大学埃尔帕索分校）；Department of Nursing, The University of Texas at El Paso, USA（护理系，德克萨斯理工大学埃尔帕索分校）；Department of Rehabilitation Sciences, The University of Texas at El Paso, USA（康复科学系，德克萨斯理工大学埃尔帕索分校）
The Biased Oracle: Assessing LLMs' Understandability and Empathy in Medical Diagnoses
Department of Chemistry and Applied Biosciences, ETH Zurich（应用生物科学系，苏黎世联邦理工学院）；Department of Computer Science, ETH Zurich（计算机科学系，苏黎世联邦理工学院）；Department of Health Sciences and Technology, ETH Zurich（健康科学与技术系，苏黎世联邦理工学院）
Suzhou Fubian Medical Technology Co., Ltd.（苏州富边医疗科技有限公司）
Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker's emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism). To this end, we propose a novel approach, …
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Florida State University（佛罗里达州立大学）；University of Washington（华盛顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Fudan University（复旦大学）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；Beijing Yuandian Technology（北京原点科技）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；WeChat AI, Tencent Inc（腾讯微信人工智能）；Meituan Group（美团集团）
How does empathy influence creative problem solving? We introduce a computational empathy intervention based on context-specific affective mimicry and perspective taking by a virtual agent appearing in the form of a well-dressed polar bear. In an online experiment with 1,006 participants randomly assigned to an emotion elicitation intervention (with a control elicitation condition and anger elicitation condition) and a computational empathy intervention (with a control virtual agent and an empathic virtual agent), we examine how anger and empathy influence participants' performance in solving …
Northeastern University（东北大学）；Swinburne University of Technology（斯威本科技大学）
Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building t…
Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济发展实验室）；SKL-IOTSC, CIS, University of Macau（澳门科学馆物联网与智慧城市研究中心）；Shandong University（山东大学）；Auckland University of Technology（技术大学（奥克兰））；University of Electronic Science and Technology of China（电子科技大学）
FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；ELLIS Institute Tübingen（ELLIS图宾根研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（图宾根人工智能中心）
