Tell Me：基于LLM的心理健康助手，集成RAG、合成对话生成与智能体规划
Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning
AI总结：
提出Tell Me系统，利用大语言模型通过RAG实现个性化对话、合成客户-治疗师对话生成以及智能体规划生成自我护理计划，旨在提供可及的心理支持而非替代专业治疗。
摘要：
我们提出Tell Me，一个心理健康系统，利用大语言模型的进展为用户和研究人员提供可访问的、上下文感知的支持。该系统集成了三个组件：(i) 一个检索增强生成（RAG）助手，用于个性化、基于知识的对话；(ii) 一个基于客户档案的合成客户-治疗师对话生成器，以促进治疗语言和数据增强的研究；以及(iii) 一个使用CrewAI实现的Well-being AI团队，生成每周自我护理计划和引导冥想音频。该系统被设计为情绪处理的反思空间，而非专业治疗的替代品。它展示了对话助手如何降低支持门槛、补充现有护理并扩大心理健康资源的可及性。为解决保密治疗数据短缺的问题，我们引入了基于客户档案的合成客户-治疗师对话生成。最后，规划器展示了一种创新的智能体工作流，用于动态自适应、个性化的自我护理，弥补了静态健康工具的局限性。我们描述了架构，展示了其功能，并报告了在精心设计的健康场景中对RAG助手的评估，包括基于LLM的自动判断和人类用户研究。这项工作突出了NLP研究人员与心理健康专业人士跨学科合作的机会，以推动人类-AI交互在健康领域的负责任创新。
英文摘要：
We present Tell Me, a mental well-being system that leverages advances in large language models to provide accessible, context-aware support for users and researchers. The system integrates three components: (i) a retrieval-augmented generation (RAG) assistant for personalized, knowledge-grounded dialogue; (ii) a synthetic client-therapist dialogue generator conditioned on client profiles to facilitate research on therapeutic language and data augmentation; and (iii) a Well-being AI crew, implemented with CrewAI, that produces weekly self-care plans and guided meditation audio. The system is designed as a reflective space for emotional processing rather than a substitute for professional therapy. It illustrates how conversational assistants can lower barriers to support, complement existing care, and broaden access to mental health resources. To address the shortage of confidential therapeutic data, we introduce synthetic client-therapist dialogue generation conditioned on client profiles. Finally, the planner demonstrates an innovative agentic workflow for dynamically adaptive, personalized self-care, bridging the limitations of static well-being tools. We describe the architecture, demonstrate its functionalities, and report evaluation of the RAG assistant in curated well-being scenarios using both automatic LLM-based judgments and a human-user study. This work highlights opportunities for interdisciplinary collaboration between NLP researchers and mental health professionals to advance responsible innovation in human-AI interaction for well-being.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
