Recent advancements in Large Language Models (LLMs) have accelerated their usage in various domains. Given the fact that psychiatric interviews are goal-oriented and structured dialogues between the professional interviewer and the interviewee, it is one of the most underexplored areas where LLMs can contribute substantial value. Here, we explore the use of LLMs for enhancing psychiatric interviews, by analyzing counseling data from North Korean defectors with traumatic events and mental health issues. Specifically, we investigate whether LLMs can (1) delineate the part of the conversation that suggests psychiatric symptoms and name the symptoms, and (2) summarize stressors and symptoms, based on the interview dialogue transcript. Here, the transcript data was labeled by mental health experts for training and evaluation of LLMs. Our experimental results show that appropriately prompted LLMs can achieve high performance on both the symptom delineation task and the summarization task. This research contributes to the nascent field of applying LLMs to psychiatric interview and demonstrates their potential effectiveness in aiding mental health practitioners.

翻译：大型语言模型（LLMs）的最新进展加速了其在多个领域的应用。鉴于精神病学访谈是具有目标导向的结构化对话，由专业访谈者与受访者之间进行，这是LLMs能贡献重要价值但尚未深入探索的领域之一。本文通过分析经历过创伤事件并存在心理健康问题的朝鲜脱北者的咨询数据，探索了利用LLMs增强精神病学访谈的可能。具体而言，我们研究了LLMs能否（1）界定对话中提示精神病症状的部分并命名症状，以及（2）基于访谈对话转录总结压力源与症状。其中，转录数据由心理健康专家标注，用于LLMs的训练与评估。实验结果表明，经过适当提示的LLMs在症状界定任务与总结任务上均能实现高性能。本研究为将LLMs应用于精神病学访谈这一新兴领域做出贡献，并展示了其在辅助心理健康从业者方面的潜在效能。