LLM safeguards inadequate for mental health conditions, study finds

A new study published on arXiv evaluates the safety of large language models (LLMs) in mental health contexts, revealing significant inadequacies in their safeguards across various DSM-5 conditions. The research found that while models perform reliably for suicide and self-harm, they fail up to 100% of the time for conditions like eating disorders, substance use disorder, and major depressive disorder. The authors advocate for clearly defined harm categories and corresponding safeguards to mitigate risks to vulnerable populations, especially with the increasing integration of these models into educational settings. AI

IMPACT Highlights critical safety gaps in LLMs for mental health applications, potentially slowing adoption in sensitive areas.

RANK_REASON Research paper published on arXiv detailing safety concerns of LLMs in mental health. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Annika Marie Schoene
• arXiv
• DSM-5
• large language models
• major depressive disorder
• substance use disorder
• suicide

• safety
• paper
• policy

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