Optimizing What Matters: AUC-Driven Learning for Robust Neural Retrieval
Authors: name: Nima Sheikholeslami, name: Erfan Hosseini, name: Patrice Bechard, name: Srivatsava Daruru, name: Sai Rajeswar
Organization: ServiceNow-AI

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.