MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialogue
This addresses mental health issues such as depression and anxiety by offering a more engaging and immersive healing approach, though it appears incremental as it builds on existing LLM capabilities.
The paper tackles the problem of ineffective mental health interventions like counseling and chatbots by proposing MIND, a multi-agent inner dialogue system using LLM agents to create immersive psychological healing environments, and finds through human experiments that it provides a more user-friendly experience than traditional methods.
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.