arXiv (OAI Expanded)
open access
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 1
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Clinician use of language models diverges from how the models are evaluated#1247996
- Benchmarking Language Models for Clinical Safety: A Primer for Mental Health Professionals#130504
- Benchmarking Language Models for Clinical Safety: A Primer for Mental Health Professionals#68022
- Supplementary Material for: Benchmarking Language Models for Clinical Safety: A Primer for Mental Health Professionals#436106
- GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models#924392
Record · ID 1258013
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive