Enhancing Psychological Counseling with Large Language Model: A
  Multifaceted Decision-Support System for Non-Professionals
Guanghui Fu Qing Zhao Jianqiang Li Dan Luo Changwei Song Wei Zhai Shuo Liu Fan Wang Yan Wang Lijuan Cheng Juan Zhang Bing Xiang Yang
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.