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New benchmark VEILED-SH evaluates AI's response to indirect self-harm expressions

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced VEILED-SH, a new benchmark designed to evaluate AI systems' ability to understand and respond to indirect expressions of self-harm and distress. The dataset includes over 10,000 prompts sourced from various contexts like counselor chats and forum posts, with a focus on culturally specific nuances, particularly from Singapore. VEILED-SH aims to address limitations in existing benchmarks by incorporating cultural variation, application context, and expert disagreement through a detailed taxonomy and multiple automated profiles for scoring. AI

IMPACT This benchmark could improve AI's ability to detect and respond appropriately to sensitive user expressions, enhancing safety in AI applications.

RANK_REASON The cluster describes a new academic paper introducing a benchmark dataset for AI safety research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark VEILED-SH evaluates AI's response to indirect self-harm expressions

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The cluster describes a new academic paper introducing a benchmark dataset for AI safety research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Leanne Tan, Keith Chua, Shaun Khoo, Onno P. Kampman, Lim Ruling Charmaine, Hazirah Hoosainsah, Kellie Yu Hui Sim, Kai He, Kenny Tsu Wei Choo, Roy Ka-Wei Lee ·

    VEILED-SH: Vulnerability Expression through Indirect Language Evaluation Dataset (Self-Harm)

    arXiv:2610.26190v2 Announce Type: replace Abstract: AI systems may miss indirect, culturally situated distress or provide support mismatched to the application context. Existing benchmarks do not jointly address cultural variation, application context, and practitioner disagreeme…

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