用AI分析对话中的焦虑水平，还能给出个性化建议。
Exploring the Panorama of Anxiety Levels: A Multi-Scenario Study Based on Human-Centric Anxiety Level Detection and Personalized Guidance
- 基于对话模拟和Transformer模型识别焦虑等级
- 分类准确率超94%，并能生成相关解释
- 适合心理健康辅助、智能咨询系统开发者
越来越多的人面临工作、生活和教育带来的压力，这些压力常导致焦虑情绪，甚至出现自杀意念的早期迹象。随着人工智能技术的发展，大语言模型已成为心理障碍检测的重要工具。然而，现有研究多仅提供分类结果，缺乏可解释性说明。为此，本研究从以人为本的角度出发，采用GPT生成的多场景模拟对话作为数据样本，利用多种基于Transformer的编码器模型构建焦虑等级分类模型。同时，借助LangChain与GPT-4构建聚焦焦虑的知识库，在分析分类结果时，能够为对话参与者提供与其情境最相关的解释与原因。实验表明，该模型在类别预测上准确率超过94%，且提供的建议高度个性化且相关性强。
原文摘要 · Abstract (English)
More and more people are experiencing pressure from work, life, and education. These pressures often lead to an anxious state of mind, or even the early symptoms of suicidal ideation. With the advancement of artificial intelligence (AI) technology, large language models have become one of the most prominent technologies. They are often used for detecting psychological disorders. However, current studies primarily provide categorization results without offering interpretable explanations for these results. To address this gap, this study adopts a person-centered perspective and focuses on GPT-generated multi-scenario simulated conversations. These simulated conversations were selected as data samples for the study. Various transformer-based encoder models were utilized to develop a classification model capable of identifying different levels of anxiety. Additionally, a knowledge base focusing on anxiety was constructed using LangChain and GPT-4. When analyzing classification results, this knowledge base was able to provide explanations and reasons most relevant to the interlocutor's anxiety situation. The study demonstrates that the proposed model achieves over 94% accuracy in categorical prediction, and the advice provided is highly personalized and relevant.
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