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New K-Bench benchmark evaluates LLM safety in mental health conversations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed K-Bench, a new benchmark designed to evaluate the safety of large language models (LLMs) in high-risk mental health conversations. The benchmark, which includes 200 multi-turn vignettes covering topics like suicide and self-harm, was calibrated by clinicians and showed that while leading models perform well in supportive conversations, their risk exploration capabilities vary significantly. A separate review indicates that most existing mental health benchmarks for LLMs rely on less robust data sources like social media or LLM-generated content, with a very small percentage utilizing protected test sets. AI

IMPACT Establishes a new standard for evaluating LLM safety in sensitive mental health applications, potentially guiding future development and deployment.

RANK_REASON The cluster contains a research paper introducing a new benchmark for LLMs in a specific domain.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New K-Bench benchmark evaluates LLM safety in mental health conversations

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The cluster contains a research paper introducing a new benchmark for LLMs in a specific domain.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Laura M. Vowels, Matthew J. Vowels, Shivali Sharma, Apoorv Jha, Rehnuma Choudhury, Wasseem El Sarraj, Rachel Francois-Walcott, Aruba Hussain, Sarah Ingram, Angela Loulopoulou, Adva Segal, Elena Volkova ·

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

    arXiv:2609.15855v1 Announce Type: cross 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-calibr…

  2. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    "Mental Health Benchmarks for Large Language Models: A Systematic Scoping Review" maps 173 benchmarks. Most rely on social media or LLM-generated data; only 2%

    "Mental Health Benchmarks for Large Language Models: A Systematic Scoping Review" maps 173 benchmarks. Most rely on social media or LLM-generated data; only 2% report hidden test sets. # LLM # MentalHealth # AI https:// doi.org/10.17605/OSF.IO/CZB7V

    LINKS osf.io/czb7v

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