Aligning Large Language Models for Enhancing Psychiatric Interviews through Symptom Delineation and Summarization

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 ca...

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Bibliographic Details
Published inarXiv.org
Main Authors Jae-hee So, Chang, Joonhwan, Kim, Eunji, Na, Junho, Choi, JiYeon, Sohn, Jy-yong, Kim, Byung-Hoon, Sang Hui Chu
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.03.2024
Subjects
Delineation
Interviews
Large language models
Mental health
Mental health care
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Summary: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.
ISSN:2331-8422