For real-world language applications, detecting an out-of-distribution (OOD) sample is helpful to alert users or reject such unreliable samples. However, modern over-parameterized language models often produce overconfident predictions for both in-distribution (ID) and OOD samples. In particular, language models suffer from OOD samples with a similar semantic representation to ID samples since these OOD samples lie near the ID manifold. A rejection network can be trained with ID and diverse outlier samples to detect test OOD samples, but explicitly collecting auxiliary OOD datasets brings an additional burden for data collection. In this paper, we propose a simple but effective method called Pseudo Outlier Exposure (POE) that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes. The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network. Our method does not require any external OOD data and can be easily implemented within off-the-shelf Transformers. A comprehensive comparison with state-of-the-art algorithms demonstrates POE's competitiveness on several text classification benchmarks.

我们提出了一种名为Pseudo Outlier Exposure（POE）的简单但有效的方法，通过顺序遮盖与ID类相关的令牌来构建一个替代性的OOD数据集，该替代性OOD样本与ID数据具有相似的表示，可以在训练拒绝网络方面发挥最有效的作用，与最先进的算法进行全面比较，在几个文本分类基准测试中证明了POE的竞争力。

使用预训练转换器的伪异常值暴露进行对外分布检测