@inproceedings{nguyen-etal-2025-large,
    title = "Do Large Language Models Align with Core Mental Health Counseling Competencies?",
    author = "Nguyen, Viet Cuong  and
      Taher, Mohammad  and
      Hong, Dongwan  and
      Possobom, Vinicius Konkolics  and
      Gopalakrishnan, Vibha Thirunellayi  and
      Raj, Ekta  and
      Li, Zihang  and
      Soled, Heather J.  and
      Birnbaum, Michael L.  and
      Kumar, Srijan  and
      De Choudhury, Munmun",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.418/",
    pages = "7488--7511",
    ISBN = "979-8-89176-195-7",
    abstract = "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"
}