A Survey on Out-of-Distribution Detection in NLP

Hao Lang, Yinhe Zheng, Yixuan Li, Jian SUN, Fei Huang, Yongbin Li

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.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

1. thoroughly fix the citation formatting
2. add recent publications on OOD detection in NLP

Assigned Action Editor: ~Vlad_Niculae2

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1675