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arXiv · 2210.11034

Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble

Hyunsoo Cho·Choonghyun Park·Jaewook Kang·Kang Min Yoo·Taeuk Kim·Sang-goo Lee
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Abstract

Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize the information from a single representation that resides in the penultimate layer to determine whether the input is anomalous or not. Although such a method is straightforward, the potential of diverse information in the intermediate layers is overlooked. In this paper, we propose a novel framework based on contrastive learning that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. Extensive experiments in various intent classification and OOD datasets demonstrate that our approach is significantly more effective than other works.

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Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo, Taeuk Kim, Sang-goo Lee. 2022-10-20. Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble. https://arxiv.org/abs/2210.11034

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