Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
This work addresses the problem of improving mental health screening for social media users by making detection more explainable and interactive, though it appears incremental as it builds on existing LLM capabilities.
The paper tackles depression detection from social media text by introducing an LLM-based system that provides interpretable diagnoses, evidence, and personalized recommendations through dialogue, achieving performance superior to traditional methods.
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.