SouLLMate：一种利用自适应大语言模型、提示工程与RAG技术增强多样化心理健康支持的应用
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques
AI总结：
本文提出SouLLMate自适应大语言模型系统，结合RAG、提示工程及KIS、PQS、SMMR等方法，提供多样化、个性化、实时的心理健康支持，并开发了基于专业标注和真实自杀倾向数据的新颖评估方法。
摘要：
心理健康问题严重影响人们的日常生活，然而许多人即使有可用的在线资源，也未能获得所需的帮助。本研究旨在通过前沿人工智能技术提供多样化、可及、无污名化、个性化且实时的心理健康支持。本研究做出以下贡献：（1）对近期心理健康支持方法进行了广泛调研，以识别普遍存在的功能和未被满足的需求。（2）引入SouLLMate，一个自适应的大语言模型驱动系统，该系统整合了大语言模型技术、Chain、检索增强生成（RAG）、提示工程和领域知识。该系统提供风险检测和主动引导对话等高级功能，并利用RAG实现个性化档案上传和对话信息抽取。（3）开发了新颖的评估方法，通过专业标注的访谈数据和真实自杀倾向数据进行初步评估和风险检测。（4）提出了关键指标摘要（KIS）、主动提问策略（PQS）和堆叠多模型推理（SMMR）方法，通过上下文敏感的响应调整、语义连贯性评估以及增强语言模型中长上下文推理的准确性来提升模型性能和可用性。本研究有助于推进心理健康支持技术的发展，有望改善全球心理健康护理的可及性和有效性。
英文摘要：
Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies. It makes the following contributions: (1) Conducting an extensive survey of recent mental health support methods to identify prevalent functionalities and unmet needs. (2) Introducing SouLLMate, an adaptive LLM-driven system that integrates LLM technologies, Chain, Retrieval-Augmented Generation (RAG), prompt engineering, and domain knowledge. This system offers advanced features such as Risk Detection and Proactive Guidance Dialogue, and utilizes RAG for personalized profile uploads and Conversational Information Extraction. (3) Developing novel evaluation approaches for preliminary assessments and risk detection via professionally annotated interview data and real-life suicide tendency data. (4) Proposing the Key Indicator Summarization (KIS), Proactive Questioning Strategy (PQS), and Stacked Multi-Model Reasoning (SMMR) methods to enhance model performance and usability through context-sensitive response adjustments, semantic coherence evaluations, and enhanced accuracy of long-context reasoning in language models. This study contributes to advancing mental health support technologies, potentially improving the accessibility and effectiveness of mental health care globally.
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。
