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.

提出了一种双提示方法，通过使用领域特定的信息和基于语言模型的一致性评估器，实现知识感知证据提取和证据总结，并综合实验证明了该方法的有效性和帮助临床医生评估心理状态进展的潜力。