Challenges of Large Language Models for Mental Health Counseling
Neo Christopher Chung George Dyer Lennart Brocki
The global mental health crisis is looming with a rapid increase in mental
disorders, limited resources, and the social stigma of seeking treatment. As
the field of artificial intelligence (AI) has witnessed significant
advancements in recent years, large language models (LLMs) capable of
understanding and generating human-like text may be used in supporting or
providing psychological counseling. However, the application of LLMs in the
mental health domain raises concerns regarding the accuracy, effectiveness, and
reliability of the information provided. This paper investigates the major
challenges associated with the development of LLMs for psychological
counseling, including model hallucination, interpretability, bias, privacy, and
clinical effectiveness. We explore potential solutions to these challenges that
are practical and applicable to the current paradigm of AI. From our experience
in developing and deploying LLMs for mental health, AI holds a great promise
for improving mental health care, if we can carefully navigate and overcome
pitfalls of LLMs.