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Study: LLMs often fail to safely handle suicidal ideation disclosures

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates how large language models (LLMs) handle disclosures of suicidal ideation (SI). The research found that LLMs often refuse to engage with SI disclosures, which can be a safety failure in itself. While informing models about the SI context or suppressing refusal increases engagement, it does not reliably lower response risk and may even increase it. A clinician-developed protocol, however, consistently reduced response risk while also decreasing refusal rates. AI

IMPACT Highlights critical safety gaps in LLM responses to sensitive user disclosures, necessitating improved refusal and engagement protocols.

RANK_REASON Academic paper on LLM safety mechanisms. [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 →

Study: LLMs often fail to safely handle suicidal ideation disclosures

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19 / 100
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Academic paper on LLM safety mechanisms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Drishti Goel, Soorya Ram Shimgekar, Jeongah Lee, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Koustuv Saha ·

    Risk and Refusal: Mechanisms of LLM Responses to Suicidal Ideation

    arXiv:2610.22715v1 Announce Type: cross Abstract: Refusal is a common safety mechanism in large language models (LLMs), yet in contexts of suicidal ideation (SI), refusing to engage can itself constitute a safety failure. We evaluate how eight instruction-tuned LLMs respond to SI…

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