mlsanityData to 7 Oct 2026

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

arXiv 2602.06031 ↗First posted 5 Feb 2026cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 79 of its 111 references that are in the map. First posted on arXiv 5 Feb 2026.

Builds on

79 of 111 references.

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026MM++: Post-Hoc Scale-Invariant Multilayer OOD Detection via Top-K Gated Feature Fusioncited by 0