Consistent Client Simulation for Motivational Interviewing-based Counseling
Beijing Institute of Technology（北京理工大学）；Singapore Management University（新加坡管理大学）；Australian National University（澳大利亚国立大学）；ThoughtFull；Singapore Institute of Technology（新加坡科技设计大学）；National Institute of Education（新加坡国立教育学院）；Singapore University of Social Sciences（新加坡社科大学）；National University of Singapore（新加坡国立大学）
Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, John Pinto, Jenny Giam, Kit Phey Leng, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Ee-peng Lim
Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client's actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and generates for each state behaviors consistent with the client's motivation, beliefs, preferred plan to change, and receptivity. By varying the client profile and receptivity, we demonstrate that consistent simulated clients for different counseling scenarios can be effectively created. Both our automatic and expert evaluations on the generated counseling sessions also show that our client simulation method achieves higher consistency than previous methods.
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…
CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration
Beijing Institute of Technology（北京理工大学）；Singapore Management University（新加坡管理大学）；Australian National University（澳大利亚国立大学）；National Institute of Education（教育学院）；Singapore University of Social Sciences（新加坡社科大学）；National University of Singapore（新加坡国立大学）
Benchmarking Motivational Interviewing Competence of Large Language Models
National Institute of Mental Health and Neuro Sciences (NIMHANS)（国家精神卫生与神经科学研究所（NIMHANS））；Indian Institute of Technology (IIT) Kharagpur（印度理工学院卡拉格普尔分校（IIT Kharagpur））
Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction
Department of Computer Science, University of Memphis（密苏里大学计算机科学系）；Veterans Affairs Health Care System（退伍军人事务医疗系统）；Department of Psychiatry and Behavioral Sciences, University of California San Francisco（旧金山大学精神病学与行为科学系）；Department of Psychology, University of Memphis（密苏里大学心理学系）；Department of Psychology, Washington State University Vancouver（华盛顿州立大学温哥华分校心理学系）
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce \textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their…
GenAI for Social Work Field Education: Client Simulation with Real-Time Feedback
School of Computing（计算学院）；Data Science The University of Hong Kong Hong Kong, China（数据科学香港大学香港）；Dept. of Mathematics The University of Hong Kong Hong Kong, China（数学系香港大学香港）；Faculty of Engineering The University of Hong Kong Hong Kong, China（工程学院香港大学香港）；Social Administration The University of Hong Kong Hong Kong, China（社会行政香港大学香港）
Distinct Profiles of Run-to-Run Score Reliability and Expert-Panel Alignment Across Four LLM Evaluators of Simulated Japanese-Language AI-to-AI Counseling
Japan National Institute of Occupational Safety and Health（日本国立职业安全卫生研究所）；Kaze To Taiyo（凯泽・太阳）；Saga Occupational Health Association（Saga职业健康协会）；Department of Pharmacy, Zikei Hospital/Zikei Institute of Psychiatry（药剂科，Zikei医院/Zikei精神医学研究所）；Department of Medical Welfare, Suzuka University of Medical Science（医疗福祉科， Suzuka医科大学）；Graduate School of Human Sciences, Ritsumeikan University（人类科学研究生院，立命馆大学）；Faculty of Nursing, National Defense Medical College（护理学部，国家防卫医疗大学）；Support Center for Students with Disabilities, Aoyama Gakuin University（残疾学生支持中心，上智大学）
Can AI Relate: Testing Large Language Model Response for Mental Health Support
New York University（纽约大学）；Grossman School of Medicine（格罗斯曼医学院）；Massachusetts Institute of Technology（麻省理工学院）；University of California, Los Angeles（加州大学洛杉矶分校）
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）
