Do Large Language Models Align with Core Mental Health Counseling Competencies?
This addresses the global shortage of mental health professionals by assessing AI's potential for counseling, but it is incremental as it benchmarks existing models without proposing new methods.
The study tackled the problem of evaluating whether Large Language Models (LLMs) align with essential mental health counseling competencies by introducing CounselingBench, a benchmark testing 22 models across five key areas, finding that frontier models exceed minimum thresholds but fall short of expert performance, with specific strengths in Intake, Assessment & Diagnosis and weaknesses in Core Counseling Attributes and Professional Practice & Ethics.
The rapid evolution of Large Language Models (LLMs) presents a promising solution to the global shortage of mental health professionals. However, their alignment with essential counseling competencies remains underexplored. We introduce CounselingBench, a novel NCMHCE-based benchmark evaluating 22 general-purpose and medical-finetuned LLMs across five key competencies. While frontier models surpass minimum aptitude thresholds, they fall short of expert-level performance, excelling in Intake, Assessment & Diagnosis but struggling with Core Counseling Attributes and Professional Practice & Ethics. Surprisingly, medical LLMs do not outperform generalist models in accuracy, though they provide slightly better justifications while making more context-related errors. These findings highlight the challenges of developing AI for mental health counseling, particularly in competencies requiring empathy and nuanced reasoning. Our results underscore the need for specialized, fine-tuned models aligned with core mental health counseling competencies and supported by human oversight before real-world deployment. Code and data associated with this manuscript can be found at: https://github.com/cuongnguyenx/CounselingBench