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Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals

Authors :
Fu, Guanghui
Zhao, Qing
Li, Jianqiang
Luo, Dan
Song, Changwei
Zhai, Wei
Liu, Shuo
Wang, Fan
Wang, Yan
Cheng, Lijuan
Zhang, Juan
Yang, Bing Xiang
Publication Year :
2023

Abstract

In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions. This situation underscores a profound need for trained psychological counselors who can enact effective mental interventions. However, the development of these professionals is often an imperative but time-consuming task. Consequently, the mobilization of non-professionals or volunteers in this capacity emerges as a pressing concern. Leveraging the capabilities of artificial intelligence, and in particular, the recent advances in large language models, offers a viable solution to this challenge. This paper introduces a novel model constructed on the foundation of large language models to fully assist non-professionals in providing psychological interventions on online user discourses. This framework makes it plausible to harness the power of non-professional counselors in a meaningful way. A comprehensive study was conducted involving ten professional psychological counselors of varying expertise, evaluating the system across five critical dimensions. The findings affirm that our system is capable of analyzing patients' issues with relative accuracy and proffering professional-level strategies recommendations, thereby enhancing support for non-professionals. This research serves as a compelling validation of the application of large language models in the field of psychology and lays the groundwork for a new paradigm of community-based mental health support.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2308.15192
Document Type :
Working Paper