Pairwise Ranking Prompting (PRP) elicits pairwise preference judgments from an LLM, which are then aggregated into a ranking, usually via classical sorting algorithms. However, judgments are noisy, order-sensitive, and sometimes intransitive, so sorting assumptions do not match the setting. Because sorting aims to recover a full permutation, truncating it to meet a call budget does not produce a dependable top-K. We thus reframe PRP reranking as active learning from noisy pairwise comparisons and show that active rankers are drop-in replacements that improve NDCG@10 per call in the call-constrained regime. Our noise-robust framework also introduces a randomized-direction oracle that uses a single LLM call per pair. This approach converts systematic position bias into zero-mean noise, enabling unbiased aggregate ranking without the cost of bidirectional calls.

翻译：成对排序提示（Pairwise Ranking Prompting, PRP）通过从大语言模型（LLM）中获取成对偏好判断，并通常借助经典排序算法将这些判断聚合为排序结果。然而，这些判断存在噪声、顺序敏感性以及有时非传递性的问题，因此排序假设并不适用于该场景。由于排序旨在恢复完整的排列，通过截断来满足调用预算无法生成可靠的top-K结果。为此，我们将PRP重排序重新定义为基于含噪成对比较的主动学习，并证明主动排序器可作为即插即用的替代方案，在调用受限场景下提升每次调用对应的NDCG@10。我们提出的噪声鲁棒框架还引入了一种随机方向预测器，每对比较仅需单次LLM调用。该方法将系统性位置偏差转化为零均值噪声，使得无需双向调用即可实现无偏聚合排序。