Summary of Do Large Language Models Align with Core Mental Health Counseling Competencies?, by Viet Cuong Nguyen et al.
Do Large Language Models Align with Core Mental Health Counseling Competencies?
by Viet Cuong Nguyen, Mohammad Taher, Dongwan Hong, Vinicius Konkolics Possobom, Vibha Thirunellayi Gopalakrishnan, Ekta Raj, Zihang Li, Heather J. Soled, Michael L. Birnbaum, Srijan Kumar, Munmun De Choudhury
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces CounselingBench, a novel benchmark evaluating Large Language Models (LLMs) in their alignment with essential counseling competencies. The study evaluates 22 LLMs across five key competencies, including Intake, Assessment & Diagnosis, Core Counseling Attributes, Professional Practice & Ethics, and Treatment Planning & Implementation. While frontier models surpass minimum aptitude thresholds, they fall short of expert-level performance. Surprisingly, medical LLMs do not outperform generalist models in accuracy, though they provide slightly better justifications while making more context-related errors. The findings highlight the challenges of developing AI for mental health counseling and underscore the need for specialized, fine-tuned models aligned with core mental health counseling competencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CounselingBench is a new way to test how well large language models can understand and help people with mental health problems. Researchers looked at 22 of these models and found that while they’re getting better at some things, they still have trouble doing others. They’re good at listening to someone’s problem, but struggle with giving advice or explaining why they made a decision. The models that were trained on medical information didn’t do much better than the general ones. This shows how hard it is to make artificial intelligence that can help people with mental health problems, and that we need to make special models that are good at this kind of thing. |
Keywords
» Artificial intelligence » Alignment
