A Survey on Out-of-Distribution Detection in NLP
It addresses the need for reliable and safe deployment of machine learning systems in NLP, but is incremental as a review.
This paper presents the first survey on out-of-distribution detection in natural language processing, categorizing recent algorithms and discussing datasets, applications, and metrics.
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.