← Back to all news
arXiv Computation and Language By Drew Rein, Patrick Oathout, Vishal Kumar, Udari Madhushani Sehwag

DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models

Read the original on arXiv Computation and Language →

The paper introduces DistressBench, a benchmark comprising 718 English clinician-authored conversations covering 23 self-harm subcategories, each evaluated against a weighted rubric across seven crisis-support dimensions. Models are scored by the fraction of criteria they satisfy, revealing that the best models meet 88.4% of weighted criteria while clinicians achieve 98.7%. The study highlights gaps in crisis response, notably that 35.3% of conversations where crisis is recognized fail to route users to human help, underscoring the need to evaluate models on supportive care rather than mere refusal.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Computation and Language
Sep 22

LLMs Anchor on Chief Complaint and Fail to Integrate Evidence in Sequential Clinical Triage

The study evaluates large language models (LLMs) on sequential emergency department triage, where acuity labels are predicted from progressively longer nurse‑patient conversations. Six LLMs were tested at five checkpoints on simulated and physician‑authored dialogues, showing a decline from moderate‑to‑substantial agreement on full records to only fair‑to‑moderate agreement at each checkpoint. The models consistently anchor on chief complaint exchanges and fail to integrate later evidence, yielding low agreement with clinicians (QWK 0.295 vs. 0.887‑0.929) and concentrating predictions on ESI‑2 and ESI‑3. whyItMatters":"The findings reveal that LLMs, despite strong offline performance, cannot reliably handle the sequential nature of real‑time triage, highlighting a critical gap for safe deployment in emergency settings."

By Dipankar Srirag, Haokai Zhao, Ashutosh Kumar, Eleanor Hopper, Michael Dalton, Quoc Dung Nguyen, Aditya Joshi, Salil S. Kanhere, Padmanesan Narasimhan
llmsbenchmarks
More like this →
arXiv AI
Sep 15

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 con...

By 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
llmsbenchmarkssafety
More like this →