CARE-MH: Towards Unified, Reproducible, and Comparable Evaluation of Mental Health LLMs

Abstract:Large language models (LLMs) are increasingly used to provide mental health support, requiring reliable evaluation of safety, empathy, and therapeutic appropriateness. However, existing mental health benchmarks are difficult to reproduce and compare due to inconsistent evaluation designs and metric definitions. We present CARE-MH, a unified framework for comparable and reproducible evaluation of mental health LLMs. Using CARE-MH, we reproduce and analyze state-of-the-art benchmarks, revealing that reproducibility depends strongly on model stability and that cross-benchmark disagreement primarily arises from differences in metric definitions. Our findings highlight the need for standardized evaluation configurations and shared metric definitions for future mental health LLM benchmarks.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2607.24754 [cs.HC]

(or arXiv:2607.24754v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2607.24754 Focus to learn more arXiv-issued DOI via DataCite

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