arXiv:2606.05308v1 Announce Type: cross Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set. PPI is provably unbiased regardless of the LLM judge's error profile. We make it applicable to hierarchical metrics like Precision@K, where annotations are per-document but the metric is per-query, by reducing the output-space computation from O(2^|C|) to O(2^K). On the ESCI benchmark, augmenting 30 human annotations with Claude 3 Sonnet judgments reduces the
The rapid advancement and adoption of LLMs for specialized tasks, coupled with the inherent cost and scalability limitations of human annotation, drives the need for reliable LLM-based evaluation methods.
This development offers a provably unbiased and scalable method for evaluating LLM performance, directly impacting the speed and accuracy of AI development and deployment.
The ability to reliably evaluate complex hierarchical LLM outputs with minimal human intervention changes how AI systems are benchmarked, refined, and deployed, particularly in ranking and retrieval applications.
- · AI developers
- · LLM providers
- · AI-driven search/recommendation platforms
- · AI researchers
- · Traditional human annotation services (for evaluation)
- · Companies relying on slow, unscalable evaluation
AI development cycles will accelerate due to faster and more reliable evaluation of complex models.
This improved evaluation will lead to more robust and higher-performing LLMs in commercial applications like search and content recommendation.
Increased reliance on LLM-based evaluation could inadvertently influence the design and optimization of future LLMs to excel on these specific metrics, potentially leading to new forms of model 'overfitting' to evaluation protocols.
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Read at arXiv cs.CL