K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk conversations remains poorly characterised. We developed K-Bench, a clinician-calibrated, protected benchmark evaluating 125 model configurations representing 33 base models from 14 providers across a fixed cohort of 200 multi-turn vignettes involving suicide, self-harm, domestic violence, substance misuse, and no-risk presentations. Synthetic patient conversations showed substantial distributional overlap with real human-AI conversations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible item comparisons from 151 clinici...
| Authors | Laura M. Vowels, Matthew J. Vowels, Shivali Sharma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 September 2026 |
| Last updated at source | 27 September 2026 |
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