Abstract
Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots, is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.- Anthology ID:
- 2023.emnlp-main.357
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5831–5857
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.357
- DOI:
- 10.18653/v1/2023.emnlp-main.357
- Cite (ACL):
- Maxime Darrin, Pablo Piantanida, and Pierre Colombo. 2023. RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5831–5857, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data (Darrin et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-main.357.pdf