LLM Judge Validation Under Sparse Overlap: From Inference to Design

Agents & Inference

arXiv

LLM Judge Validation Under Sparse Overlap: From Inference to Design

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

Summary A

Mistral Large quota or rate limit — check usage and plan. Original headline: LLM Judge Validation Under Sparse Overlap: From Inference to Design

Summary B

At 5% pairwise human-label overlap, LLM-judge validation can make wrong deployment decisions 25% of the time and pick the wrong best judge among ten candidates 65% of the time. If you ship evaluator models, sparse overlap is not a bookkeeping detail: budget for roughly 25% overlap for non-borderline judges and allocate overlap stratified by informative slices, because that can halve false rejections versus random sampling.

0 picks