AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search
How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone of...
Summary
How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone of search ranking systems which is needed for training data preparation, NDCG evaluation, and root cause analysis. However, human annotation is slow and off-the-shelf LLMs suffer from accuracy on domain-specific tasks. We propose a calibrated model c
Why it matters
This is part of the steady stream of AI work that reshapes how researchers and builders think about what’s possible. The full details are in the original source below — worth reading directly rather than relying on a brief summary.
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The primary source has the full paper, announcement, or reporting:
→ https://arxiv.org/abs/2606.25871v1
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