Pretrained Transformers (PT) have been shown to improve Out of Distribution (OOD) robustness than traditional models such as Bag of Words (BOW), LSTMs, Convolutional Neural Networks (CNN) powered by Word2Vec and Glove embeddings. How does the robustness comparison hold in a real world setting where some part of the dataset can be noisy? Do PT also provide more robust representation than traditional models on exposure to noisy data? We perform a comparative study on 10 models and find an empirical evidence that PT provide less robust representation than traditional models on exposure to noisy data. We investigate further and augment PT with an adversarial filtering (AF) mechanism that has been shown to improve OOD generalization. However, increase in generalization does not necessarily increase robustness, as we find that noisy data fools the AF method powered by PT.

翻译：受过训练的变异器(PT)比用Word2Vec 和 Glove 嵌入器驱动的传统模型(Words Bag (BOW)、LSTMS、LSTMS、Culvial神经网络(CNN)等传统模型(Word2Vec 和 Glove 嵌入器),显示比传统的模型(OOOD)更可靠。在真实的环境下,数据集的某些部分会吵动,强性比较如何维持?PT是否也比传统模型更可靠地表示接触噪音数据?我们对10个模型进行比较研究,并找到经验性证据,证明PT在接触吵闹数据方面比传统模型更不可靠。我们进一步调查并增加对抗性过滤(AF)机制,以改进OOD的通用性。然而,一般化的增加并不一定增加强性,因为我们发现噪音数据会忽略PT所支配的AF方法。