CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmark of Large Language Models in Mental Health Counseling

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.