A Dual-Prompting for Interpretable Mental Health Language Models

arXiv:2402.14854v1 Announce Type: new
Abstract: Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability.The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific …

abstract analysis arxiv clinicians cs.ai cs.cl demand health interpretability language language models large language large language models llms mental health monitoring practical prompting tools type utility