The crisis of mental health issues is escalating. Effective counseling serves as a critical lifeline for individuals suffering from conditions like PTSD, stress, etc. Therapists forge a crucial therapeutic bond with clients, steering them towards positivity. Unfortunately, the massive shortage of professionals, high costs, and mental health stigma pose significant barriers to consulting therapists. As a substitute, Virtual Mental Health Assistants (VMHAs) have emerged in the digital healthcare space. However, most existing VMHAs lack the commonsense to understand the nuanced sentiments of clients to generate effective responses. To this end, we propose EmpRes, a novel sentiment-guided mechanism incorporating commonsense awareness for generating responses. By leveraging foundation models and harnessing commonsense knowledge, EmpRes aims to generate responses that effectively shape the client's sentiment towards positivity. We evaluate the performance of EmpRes on HOPE, a benchmark counseling dataset, and observe a remarkable performance improvement compared to the existing baselines across a suite of qualitative and quantitative metrics. Moreover, our extensive empirical analysis and human evaluation show that the generation ability of EmpRes is well-suited and, in some cases, surpasses the gold standard. Further, we deploy EmpRes as a chat interface for users seeking mental health support. We address the deployed system's effectiveness through an exhaustive user study with a significant positive response. Our findings show that 91% of users find the system effective, 80% express satisfaction, and over 85.45% convey a willingness to continue using the interface and recommend it to others, demonstrating the practical applicability of EmpRes in addressing the pressing challenges of mental health support, emphasizing user feedback, and ethical considerations in a real-world context.

翻译：心理健康问题危机正日益加剧。有效的心理咨询为遭受创伤后应激障碍、压力等病症困扰的个体提供了关键的生命线。治疗师与来访者建立至关重要的治疗联盟，引导其走向积极方向。然而，专业人员的严重短缺、高昂费用以及心理健康污名化构成了咨询治疗师的重要障碍。作为替代方案，虚拟心理健康助手（VMHAs）已在数字医疗领域兴起。但现有的大多数VMHAs缺乏常识来理解来访者的细微情感，从而无法生成有效的回应。为此，我们提出EmpRes——一种新颖的情感引导机制，融合常识感知以生成回应。通过利用基础模型并整合常识知识，EmpRes旨在生成能有效引导来访者情感趋向积极的回应。我们在基准咨询数据集HOPE上评估EmpRes的性能，观察到其在一系列定性与定量指标上相比现有基线模型均有显著提升。此外，我们广泛的实证分析和人工评估表明，EmpRes的生成能力适配良好，在某些情况下甚至超越了黄金标准。进一步地，我们将EmpRes部署为面向寻求心理健康支持用户的聊天界面。通过详尽的用户研究，我们验证了该部署系统的有效性，并获得了显著的积极反馈。我们的研究结果显示：91%的用户认为系统有效，80%表示满意，超过85.45%的用户表达了继续使用该界面并将其推荐给他人的意愿。这证明了EmpRes在应对心理健康支持紧迫挑战方面的实际适用性，同时强调了现实场景中用户反馈与伦理考量的重要性。