Challenges of Auditing: Variability in Outputs of Large Language Models for Health
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Computer Science > Computation and Language
arXiv:2609.16590 (cs)
[Submitted on 15 Sep 2026]
Title:Challenges of Auditing: Variability in Outputs of Large Language Models for Health
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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 typically rely on APIs while consumers interact through chatbot interfaces, these discrepancies limit evaluation validity. Our findings underscore an urgent need for model providers to enable faithful replication of consumer experiences and settings for rigorous audits.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.16590 [cs.CL] |
| (or arXiv:2609.16590v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16590
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Challenges of Auditing: Variability in Outputs of Large Language Models for Health, by Yuan Pu and 6 other authors
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