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.

翻译：本文提出了一种基于大语言模型的新型抑郁症检测系统，该系统兼具可解释性与交互性。它不仅提供诊断结果，还能基于与用户的自然语言对话，给出诊断依据和个性化建议。我们解决了大量文本处理等挑战，并整合了专业诊断标准。我们的系统在多种场景下均优于传统方法，并通过案例研究进行了验证。