MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models

ArXi:2604.17730v1 Announce Type: new Large language models (LLMs) are increasingly explored as scalable tools for mental health counseling, yet evaluating their safety remains challenging due to the interactional and context-dependent nature of clinical harm. Existing evaluation frameworks predominantly assess isolated responses using coarse-grained taxonomies or static datasets, limiting their ability to diagnose how harms emerge and accumulate over multi-turn counseling interactions. In this work, we.