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K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Paper recorded by Signals 4 on 2026-09-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15

Category: cs.AI · 人工智能 · first seen 2026-09-15

Abstract

% !TEX root = ../main.tex 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 su

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#73 most recent of 300 cs.AI papers we have recorded · ↑ newer: LongAgent: History-Guided Agentic Search for Longitudinal Outcome Pred · ↓ older: Before You Poll with LLMs: A Deliberative Diagnostic Framework
Cite this page: K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations: the #73 most recent of 300 cs.AI papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/k-bench-a-clinically-calibrated-benchmark-for-evaluating-large-language-models-i.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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