CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmark of Large Language Models in Mental Health Counseling
Journal:
arXiv
Published Date:
Jun 10, 2025
Abstract
Large language models (LLMs) are increasingly proposed for use in mental
health support, yet their behavior in realistic counseling scenarios remains
largely untested. We introduce CounselBench, a large-scale benchmark developed
with 100 mental health professionals to evaluate and stress-test LLMs in
single-turn counseling. The first component, CounselBench-EVAL, contains 2,000
expert evaluations of responses from GPT-4, LLaMA 3, Gemini, and online human
therapists to real patient questions. Each response is rated along six
clinically grounded dimensions, with written rationales and span-level
annotations. We find that LLMs often outperform online human therapists in
perceived quality, but experts frequently flag their outputs for safety
concerns such as unauthorized medical advice. Follow-up experiments show that
LLM judges consistently overrate model responses and overlook safety issues
identified by human experts. To probe failure modes more directly, we construct
CounselBench-Adv, an adversarial dataset of 120 expert-authored counseling
questions designed to trigger specific model issues. Evaluation across 2,880
responses from eight LLMs reveals consistent, model-specific failure patterns.
Together, CounselBench establishes a clinically grounded framework for
benchmarking and improving LLM behavior in high-stakes mental health settings.
