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arXiv · 2602.06031

AP-OOD: Attention Pooling for Out-of-Distribution Detection

Claus Hofmann·Christian Huber·Bernhard Lehner·Daniel Klotz·Sepp Hochreiter·Werner Zellinger
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Abstract

Out-of-distribution (OOD) detection, which maps high-dimensional data into a scalar OOD score, is critical for the reliable deployment of machine learning models. A key challenge in recent research is how to effectively leverage and aggregate token embeddings from language models to obtain the OOD score. In this work, we propose AP-OOD, a novel OOD detection method for natural language that goes beyond simple average-based aggregation by exploiting token-level information. AP-OOD is a semi-supervised approach that flexibly interpolates between unsupervised and supervised settings, enabling the use of limited auxiliary outlier data. Empirically, AP-OOD sets a new state of the art in OOD detection for text: in the unsupervised setting, it reduces the FPR95 (false positive rate at 95% true positives) from 27.84% to 4.67% on XSUM summarization, and from 77.08% to 70.37% on WMT15 En-Fr translation.

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Claus Hofmann, Christian Huber, Bernhard Lehner, Daniel Klotz, Sepp Hochreiter, Werner Zellinger. 2026-02-05. AP-OOD: Attention Pooling for Out-of-Distribution Detection. https://arxiv.org/abs/2602.06031

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