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Recently, there has been a growing interest in utilizing large languagemodels (LLMs) in mental health research, with studies showcasing theirremarkable capabilities, such as disease detection. However, there is currentlya lack of a comprehensive benchmark for evaluating the capability of LLMs inthis domain. Therefore, we address this gap by introducing the firstcomprehensive benchmark tailored to the unique characteristics of the mentalhealth domain. This benchmark encompasses a total of six sub-tasks, coveringthree dimensions, to systematically assess the capabilities of LLMs in therealm of mental health. We have designed corresponding concise prompts for eachsub-task. And we comprehensively evaluate a total of eight advanced LLMs usingour benchmark. Experiment results not only demonstrate significant room forimprovement in current LLMs concerning mental health but also unveil potentialdirections for future model optimization.

PsyEval: A Comprehensive Large Language Model Evaluation Benchmark for Mental Health