阅读、诊断与聊天：面向社交媒体中可解释且交互式的大语言模型增强抑郁检测
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
AI总结：
本文提出一种可解释且交互式的大语言模型抑郁检测系统，提供诊断、证据及个性化建议，优于传统方法。
摘要：
本文提出了一种基于大语言模型的新型抑郁检测系统，该系统兼具可解释性与交互性。它不仅提供诊断结果，还基于与用户的自然语言对话提供诊断证据和个性化建议。我们解决了处理大量文本等挑战，并整合了专业诊断标准。我们的系统在各种设置下均优于传统方法，并通过案例研究进行了展示。
英文摘要：
This paper proposes a new depression detection system based on LLMs that is both interpretable and interactive. It not only provides a diagnosis, but also diagnostic evidence and personalized recommendations based on natural language dialogue with the user. We address challenges such as the processing of large amounts of text and integrate professional diagnostic criteria. Our system outperforms traditional methods across various settings and is demonstrated through case studies.
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。
