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Proposes reframing Pairwise Ranking Prompting (PRP) reranking as active learning from noisy pairwise comparisons, improving NDCG@10 per call under budget constraints, and introduces a randomized-direction oracle that reduces LLM calls per pair.
This paper reframes pairwise ranking prompting as active learning from noisy comparisons, introducing a noise-robust framework with a randomized-direction oracle to improve ranking quality under call constraints and address position bias.