  State-of-the-art models can perform well in controlled environments, but they
often struggle when presented with out-of-distribution (OOD) examples, making
OOD detection a critical component of NLP systems. In this paper, we focus on
highlighting the limitations of existing approaches to OOD detection in NLP.
Specifically, we evaluated eight OOD detection methods that are easily
integrable into existing NLP systems and require no additional OOD data or
model modifications. One of our contributions is providing a well-structured
research environment that allows for full reproducibility of the results.
Additionally, our analysis shows that existing OOD detection methods for NLP
tasks are not yet sufficiently sensitive to capture all samples characterized
by various types of distributional shifts. Particularly challenging testing
scenarios arise in cases of background shift and randomly shuffled word order
within in domain texts. This highlights the need for future work to develop
more effective OOD detection approaches for the NLP problems, and our work
provides a well-defined foundation for further research in this area.
