New framework enables LLM drift detection without user data

A new framework called ProxyDrift has been developed to address the challenge of monitoring and adapting large-scale LLM applications without direct access to sensitive user data. This system utilizes non-PII proxy representations, which are structured descriptors derived from LLM-based classification of user interactions, to detect and measure data drift. ProxyDrift also enables the construction and refreshment of offline evaluation sets, ensuring continuous monitoring and targeted synthetic data generation while maintaining user privacy. Experiments demonstrate strong consistency and indistinguishability of synthetic queries from real ones, with a tight alignment to production traffic. AI

IMPACT Enables continuous monitoring and adaptation of large-scale LLM deployments without compromising user privacy.

RANK_REASON The cluster contains an academic paper detailing a new framework for LLM applications. [lever_c_demoted from research: ic=1 ai=1.0]

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• LLM
• ProxyDrift

• paper
• infra

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