用多智能体对话模拟内心交流，提升心理疗愈沉浸感。
MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialogue
- 设计多角色智能体在预设框架内对话，模拟真实心理互动。
- 真人实验显示，用户对MIND的体验友好度显著高于传统方式。
- 适合心理健康应用开发与人机情感交互研究者参考。
当今竞争社会中，抑郁、焦虑等心理健康问题日益严重。传统心理咨询和聊天机器人难以有效参与，常提供缺乏情感深度的通用回复。尽管大语言模型（LLMs）具备更拟人化交互的潜力，仍难以捕捉细微情绪变化，亟需具备类人适应性与温度。为此，我们提出MIND（Multi-agent INner Dialogue）新范式，构建更沉浸的心理疗愈环境。基于LLM智能体强大的生成与角色扮演能力，我们在预定义的治疗框架中分配不同角色，使智能体间展开交互式内心对话，从而为用户提供沉浸式疗愈体验。我们在多种真实疗愈维度开展大规模真人实验，结果表明MIND相比传统范式更具用户友好性，验证了其有效利用LLMs在心理疗愈中的巨大潜力。
原文摘要 · Abstract (English)
Mental health issues are worsening in today's competitive society, such as depression and anxiety. Traditional healings like counseling and chatbots fail to engage effectively, they often provide generic responses lacking emotional depth. Although large language models (LLMs) have the potential to create more human-like interactions, they still struggle to capture subtle emotions. This requires LLMs to be equipped with human-like adaptability and warmth. To fill this gap, we propose the MIND (Multi-agent INner Dialogue), a novel paradigm that provides more immersive psychological healing environments. Considering the strong generative and role-playing ability of LLM agents, we predefine an interactive healing framework and assign LLM agents different roles within the framework to engage in interactive inner dialogues with users, thereby providing an immersive healing experience. We conduct extensive human experiments in various real-world healing dimensions, and find that MIND provides a more user-friendly experience than traditional paradigms. This demonstrates that MIND effectively leverages the significant potential of LLMs in psychological healing.
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