Depression Detection with Novel Large Language Model Methods

Abstract

Recent advancements in machine learning and natural language processing have shown significant potential in aiding mental health diagnostics. In this paper, we specifically provide novel approaches to depression identification using large language models (LLMs). First, we employ the LLM2Vec architecture for text-only classification. Additionally, we explore the potential of multimodal LLMs by incorporating text, audio, and visual inputs, aiming to enhance diagnostic accuracy through a more comprehensive analysis of multimodal data. Our results demonstrate that text-only models perform well above baseline performances, while multimodal data shows promise for achieving a more nuanced understanding of a patient’s mental state. This work lays the groundwork for future research in developing more robust and effective tools for mental health assessment using LLMs.