arXiv:2606.15474v1 Announce Type: new Abstract: Continuous evaluation of LLM products relies on a strong LLM judge treated as ground truth: a cheap monitor scores every interaction and a team is paged when the score drifts down. But the judge is itself a model behind an API, and a silent version bump or scoring-prompt update changes how it scores -- so every drift alarm is ambiguous between a worse product and a changed judge. We resolve the ambiguity with a fixed, human-labeled anchor set that the current judge re-scores at a steady interleave, a second betting e-process on the judge-versus-h
The increasing reliance on LLM judges for continuous product evaluation necessitates robust methods to distinguish between product degradation and evaluation tool drift.
This development addresses a critical ambiguity in LLM product development, ensuring accurate performance monitoring and preventing misallocation of engineering resources.
The proposed method introduces a mechanism for anytime-valid attribution, allowing developers to confidently identify whether product performance or the evaluation system itself is changing.
- · AI product developers
- · LLM evaluation platforms
- · Companies using LLM judges
- · Companies with opaque LLM evaluation pipelines
- · Inefficient LLM product teams
Improved reliability and efficiency in LLM product development and continuous integration/continuous deployment pipelines.
Faster iteration cycles and more stable performance for LLM-powered applications across various industries.
Enhanced trust in automated evaluation systems, potentially accelerating the adoption of complex AI functionalities in critical sectors.
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Read at arXiv cs.AI