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Computer Science > Computation and Language

arXiv:2305.03236 (cs)
[Submitted on 5 May 2023 (v1), last revised 27 Dec 2023 (this version, v2)]

Title:A Survey on Out-of-Distribution Detection in NLP

Authors:Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, Yongbin Li
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Abstract: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.
Comments: TMLR
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2305.03236 [cs.CL]
  (or arXiv:2305.03236v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2305.03236

Submission history

From: Hao Lang [view email]
[v1] Fri, 5 May 2023 01:38:49 UTC (100 KB)
[v2] Wed, 27 Dec 2023 07:15:20 UTC (482 KB)
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