Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks

Mateusz Baran, Joanna Baran, Mateusz Wójcik, Maciej Zięba, Adam Gonczarek

Abstract
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.
Anthology ID:
2023.acl-srw.20
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Vishakh Padmakumar, Gisela Vallejo, Yao Fu
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
119–129
Language:
URL:
https://aclanthology.org/2023.acl-srw.20
DOI:
10.18653/v1/2023.acl-srw.20
Bibkey:
Cite (ACL):
Mateusz Baran, Joanna Baran, Mateusz Wójcik, Maciej Zięba, and Adam Gonczarek. 2023. Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 119–129, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks (Baran et al., ACL 2023)
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PDF:
https://preview.aclanthology.org/naacl24-info/2023.acl-srw.20.pdf
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