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

• URL: http://arxiv.org/abs/2403.17428v1
• Date: Tue, 26 Mar 2024 06:50:04 GMT
• Title: Aligning Large Language Models for Enhancing Psychiatric Interviews through Symptom Delineation and Summarization
• Authors: Jae-hee So, Joonhwan Chang, Eunji Kim, Junho Na, JiYeon Choi, Jy-yong Sohn, Byung-Hoon Kim, Sang Hui Chu,
• Abstract summary: This research contributes to the nascent field of applying Large Language Models to psychiatric interviews. We analyze counseling data from North Korean defectors with traumatic events and mental health issues. Our experimental results show that appropriately prompted LLMs can achieve high performance on both the symptom delineation task and the summarization task.
• Score: 13.77580842967173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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