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LLM health advice varies by access mode, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv highlights significant variability in the outputs of large language models when used for health-related queries. Researchers found that different access modes, such as direct API use versus chatbot interfaces like ChatGPT and ChatGPT Health, produce systematically different results. This discrepancy poses a challenge to the validity of current AI model evaluations, which often rely on API access while consumers interact through user-friendly interfaces. The study emphasizes the critical need for model providers to allow for accurate replication of consumer experiences and settings to enable robust auditing and ensure reliable health advice from AI. AI

IMPACT Highlights critical need for standardized auditing of LLMs in health to ensure reliable advice.

RANK_REASON Research paper published on arXiv detailing findings about LLM variability. [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 →

LLM health advice varies by access mode, study finds

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Research paper published on arXiv detailing findings about LLM variability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yuan Pu, Yewon Chang, Furong Jia, Xunjian Yin, Jessica Ma, Ayman Ali, Monica Agrawal ·

    Challenges of Auditing: Variability in Outputs of Large Language Models for Health

    arXiv:2609.16590v1 Announce Type: new Abstract: People increasingly use frontier AI models for health advice, but via different access modes (e.g., ChatGPT, ChatGPT Health, APIs) with varying settings. Here, we find systematic differences across access modes. Because evaluations …

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