A Dual-Prompting for Interpretable Mental Health Language Models
This addresses the need for interpretable tools to aid clinicians in mental health assessment, but it appears incremental as it builds on existing shared task frameworks.
The paper tackles the problem of limited interpretability in AI-based mental health monitoring tools by proposing a dual-prompting approach for evidence extraction and summarization, showing performance improvements in suicidality analysis.
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 LLM; and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach's potential to aid clinicians in assessing mental state progression.