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A Survey on Out-of-Distribution Detection in NLP

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arxiv 2305.03236 v2 pith:ML6HLHMX submitted 2023-05-05 cs.CL cs.AIcs.LG

Hao Lang , Yinhe Zheng , Yixuan Li , Jian Sun , Fei Huang , Yongbin Li This is my paper
classification cs.CLcs.AIcs.LG
keywords datadetectionunavailableavailablefirstlabelout-of-distributionrecent
verification ladder T0 review T1 audit T2 compute T3 formal
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Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past years. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches. First, we provide a formal definition of OOD detection and discuss several related fields. We then categorize recent algorithms into three classes according to the data they used: (1) OOD data available, (2) OOD data unavailable + in-distribution (ID) label available, and (3) OOD data unavailable + ID label unavailable. Third, we introduce datasets, applications, and metrics. Finally, we summarize existing work and present potential future research topics.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ACE and Diverse Generalization via Selective Disagreement

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    ACE learns an ensemble of classifiers that agree on labeled data but confidently and selectively disagree on target-distribution data, recovering diverse human-interpretable concepts under complete spurious correlation.

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    cs.CL 2025-06 conditional novelty 5.0 of 10

    CPP improves generalized intent discovery by using LLM-generated prototypes and verbalizers plus consistency and cross-prediction losses, reporting SOTA on Banking and CLINC without statistical validation.

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