优化关键指标：面向鲁棒神经检索的AUC驱动学习
Optimizing What Matters: AUC-Driven Learning for Robust Neural Retrieval
AI总结：
针对现有双编码器检索器的NCE目标与AUC脱节、导致下游任务性能不佳的问题，提出等价于最大化AUC的MW损失，理论与实验均证明其能提升检索器校准性与判别力，适用于RAG等高风险场景。
摘要：
双编码器检索器基于的核心原理是：对于给定查询，相关文档的得分应高于不相关文档。然而，作为对比损失（Contrastive Loss）基础的主流噪声对比估计（Noise Contrastive Estimation, NCE）目标，优化的是一种软化的排序代理指标，我们严格证明该指标从根本上忽略了分数分离质量，且与AUC无关。这种不匹配会导致检索增强生成（RAG）等下游任务出现校准不佳、性能次优的问题。为解决这一根本局限，我们提出MW损失——一种最大化曼-惠特尼U统计量的新型训练目标，该统计量在数学上等价于ROC曲线下面积（Area under the ROC Curve, AUC）。MW损失通过最小化分数差的二元交叉熵，促使每个正负样本对得到正确排序。我们提供了理论保证，证明MW损失直接是AoC的上界，能让优化过程与检索目标更好地对齐。我们进一步提出，ROC曲线和AUC可作为天然的无阈值诊断工具，用于评估检索器的校准效果和排序质量。实验结果表明，采用MW损失训练的检索器在AUC和标准检索指标上始终优于对比学习方法训练的同类检索器。我们的实验证明，MW损失在实证上是对比损失的更优替代方案，能为RAG等高风险应用生成校准效果更好、判别能力更强的检索器。
英文摘要：
Dual-encoder retrievers depend on the principle that relevant documents should score higher than irrelevant ones for a given query. Yet the dominant Noise Contrastive Estimation (NCE) objective, which underpins Contrastive Loss, optimizes a softened ranking surrogate that we rigorously prove is fundamentally oblivious to score separation quality and unrelated to AUC. This mismatch leads to poor calibration and suboptimal performance in downstream tasks like retrieval-augmented generation (RAG). To address this fundamental limitation, we introduce the MW loss, a new training objective that maximizes the Mann-Whitney U statistic, which is mathematically equivalent to the Area under the ROC Curve (AUC). MW loss encourages each positive-negative pair to be correctly ranked by minimizing binary cross entropy over score differences. We provide theoretical guarantees that MW loss directly upper-bounds the AoC, better aligning optimization with retrieval goals. We further promote ROC curves and AUC as natural threshold free diagnostics for evaluating retriever calibration and ranking quality. Empirically, retrievers trained with MW loss consistently outperform contrastive counterparts in AUC and standard retrieval metrics. Our experiments show that MW loss is an empirically superior alternative to Contrastive Loss, yielding better-calibrated and more discriminative retrievers for high-stakes applications like RAG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
