PRECISE: A Statistical Framework for Reducing LLM Bias in Search and Ranking Evaluations
By
[Submitted on 26 Jan 2026]
A bagel you'd recommend to a friend without hedging.
Summary
This paper presents PRECISE, a statistical framework that extends Prediction-Powered Inference (PPI) to combine minimal human annotations with LLM judgments for evaluating search, ranking, and RAG systems. The method addresses LLM bias by using as few as 100 human-annotated queries and 10,000 unlabeled examples to produce reliable metric estimates, significantly reducing annotation requirements. It reduces computational complexity from O(2^|C|) to O(2^K) and demonstrates reduced variance for Precision@K metrics while correcting LLM bias in low-resource settings.
Key quotes
· 4 pulledWe present a statistical framework extending Prediction-Powered Inference (PPI) that combines minimal human annotations with LLM judgments to produce reliable estimates of metrics which require sub-instance annotations.
Our method requires as few as 100 human-annotated queries and 10,000 unlabeled examples, reducing annotation requirements significantly compared to traditional approaches.
By reformulating the metric-integration space, we reduced the computational complexity from O(2^|C|) to O(2^K), where |C| represents corpus size (in order of millions).
Detailed experiments across prominent retrieval datasets demonstrate that our method reduces the variance of estimates for the business-critical Precision@K metric, while effectively correcting for LLM bias in low-resource settings.
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