Aligning Large Language Models for Enhancing Psychiatric Interviews
  through Symptom Delineation and Summarization
Jae-hee So Joonhwan Chang Eunji Kim Junho Na JiYeon Choi Jy-yong Sohn Byung-Hoon Kim Sang Hui Chu
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.