Illuminate：一种利用提示工程进行抑郁症检测、可解释分析与主动治疗的新方法
Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering
AI总结：
本文提出一种基于大语言模型和提示工程的抑郁症检测与治疗方法，通过少样本提示和CBT模块实现可解释分析及主动治疗，并验证了其有效性。
摘要：
本文介绍了一种利用先进大语言模型（LLMs）进行抑郁症检测和治疗的新范式，这些模型包括生成式预训练Transformer 4（GPT-4）、Llama 2 chat和Gemini。这些LLM通过专门设计的提示进行微调，以诊断、解释抑郁症并建议治疗干预措施。一种独特的少样本提示方法增强了模型基于DSM-5标准分析和解释抑郁症状的能力。在交互阶段，模型参与共情对话管理，借鉴PsychDB和认知行为疗法（CBT）指南等资源，促进与重度抑郁症患者的支持性互动。此外，研究还引入了Illuminate数据库，该数据库包含多种CBT模块，有助于个性化治疗推荐。该研究使用F1分数、精确率、召回率、余弦相似度和面向召回的Gisting评估替身（ROUGE）等指标在不同测试集上评估LLM性能，证明了其有效性。这种综合方法将前沿人工智能与成熟的心理方法相结合，为心理健康护理提供了新的可能性，并展示了LLM在革新抑郁症诊断和治疗策略方面的潜力。
英文摘要：
This paper introduces a novel paradigm for depression detection and treatment using advanced Large Language Models (LLMs): Generative Pre-trained Transformer 4 (GPT-4), Llama 2 chat, and Gemini. These LLMs are fine-tuned with specialized prompts to diagnose, explain, and suggest therapeutic interventions for depression. A unique few-shot prompting method enhances the models' ability to analyze and explain depressive symptoms based on the DSM-5 criteria. In the interaction phase, the models engage in empathetic dialogue management, drawing from resources like PsychDB and a Cognitive Behavioral Therapy (CBT) Guide, fostering supportive interactions with individuals experiencing major depressive disorders. Additionally, the research introduces the Illuminate Database, enriched with various CBT modules, aiding in personalized therapy recommendations. The study evaluates LLM performance using metrics such as F1 scores, Precision, Recall, Cosine similarity, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) across different test sets, demonstrating their effectiveness. This comprehensive approach blends cutting-edge AI with established psychological methods, offering new possibilities in mental health care and showcasing the potential of LLMs in revolutionizing depression diagnosis and treatment strategies.
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。
