标题： MoPHES：利用设备端大语言模型作为代理进行移动心理健康的评估与支持 显示英文标题

标题： MoPHES:Leveraging on-device LLMs as Agent for Mobile Psychological Health Evaluation and Support

摘要： 2022年世界心理健康报告呼吁全球心理健康护理改革，在焦虑和抑郁等影响全球近十亿人的问题日益普遍的背景下。传统面对面治疗无法满足这一需求，而且污名化情况使问题更加严重。尽管通用大语言模型（LLMs）为基于人工智能的心理健康解决方案提供了效率，但由于缺乏专业微调，它们表现不佳。现有的基于LLM的心理健康聊天机器人可以进行共情对话，但它们忽略了实时用户心理状态评估，而这是专业咨询的关键。本文提出了MoPHES，一个整合心理状态评估、对话支持和专业治疗建议的框架。在此框架下开发的代理使用两个微调过的MiniCPM4-0.5B LLM：一个在心理健康状况数据集上进行微调，以评估用户的心理状态并预测焦虑和抑郁的严重程度；另一个在多轮对话上进行微调，以处理与用户的对话。通过利用用户心理状态的洞察，我们的代理提供更定制的支持和专业的治疗建议。这两个模型也直接部署在移动设备上，以提高用户便利性并保护用户隐私。此外，为了评估MoPHES与其他LLMs的性能，我们开发了一个基准测试，用于自动评估心理状态预测和多轮咨询对话，该基准测试包括全面的评估指标、数据集和方法。 显示英文摘要

摘要： The 2022 World Mental Health Report calls for global mental health care reform, amid rising prevalence of issues like anxiety and depression that affect nearly one billion people worldwide. Traditional in-person therapy fails to meet this demand, and the situation is worsened by stigma. While general-purpose large language models (LLMs) offer efficiency for AI-driven mental health solutions, they underperform because they lack specialized fine-tuning. Existing LLM-based mental health chatbots can engage in empathetic conversations, but they overlook real-time user mental state assessment which is critical for professional counseling. This paper proposes MoPHES, a framework that integrates mental state evaluation, conversational support, and professional treatment recommendations. The agent developed under this framework uses two fine-tuned MiniCPM4-0.5B LLMs: one is fine-tuned on mental health conditions datasets to assess users' mental states and predict the severity of anxiety and depression; the other is fine-tuned on multi-turn dialogues to handle conversations with users. By leveraging insights into users' mental states, our agent provides more tailored support and professional treatment recommendations. Both models are also deployed directly on mobile devices to enhance user convenience and protect user privacy. Additionally, to evaluate the performance of MoPHES with other LLMs, we develop a benchmark for the automatic evaluation of mental state prediction and multi-turn counseling dialogues, which includes comprehensive evaluation metrics, datasets, and methods.