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LLMs fail to adapt to eating disorder queries, study finds

By PulseAugur Editorial · [2 sources] ·

A new research paper evaluates how Large Language Models (LLMs) respond to queries related to eating disorders, finding that specific linguistic cues can lead to unsafe or self-harming advice. In consultation with clinical experts, the study identified patterns where LLMs uncritically adapt to problematic user inputs. This research highlights the risks associated with users seeking support from LLMs for sensitive health issues. AI

IMPACT Highlights risks of LLMs providing unsafe advice for sensitive health topics, underscoring the need for better safety guardrails.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings.

Read on arXiv cs.AI →

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

LLMs fail to adapt to eating disorder queries, study finds

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The cluster contains an academic paper published on arXiv detailing research findings.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Giulia Pucci, Emily Hemendinger, Ruizhe Li, Gavin Abercrombie, Tanvi Dinkar, Arabella Sinclair ·

    Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback

    arXiv:2606.02444v1 Announce Type: new Abstract: Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems. Although these systems are not designed to provide cl…

  2. arXiv cs.AI TIER_1 English(EN) · Arabella Sinclair ·

    Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback

    Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems. Although these systems are not designed to provide clinical advice, their perceived expertise, neutra…

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