SST 2
Related Papers
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level
Larger language models have higher accu- racy on average, but are they better on ev- ery single instance (datapoint)? Some work suggests larger models have higher out-of- distribution robustness, while …...
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples
AbstractRecent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show that adversarial …...
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
AbstractDespite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available. …...
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation
AbstractLarge-scale pretrained language models have led to significant improvements in Natural Language Processing. Unfortunately, they come at the cost of high computational and storage requirements that complicate their deployment on …...
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
AbstractExisting studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool …...