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.

本研究探讨了利用大型语言模型 (Large Language Models, LLMs) 增强精神科访谈的应用，通过分析来自朝鲜叛逃者的咨询数据，研究其心理创伤和精神健康问题。研究结果显示，妥善引导的 LLMs 在症状划定和总结任务上取得了较高的性能，为将 LLMs 应用于精神科访谈领域的新兴研究做出了贡献，并展示了其对于协助心理健康专业人士的潜在有效性。