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

Understand

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.