Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

Recent work has shown that pre-trained language models such as BERT improve
robustness to spurious correlations in the dataset. Intrigued by these results, we find that the …

Shortcut Learning of Large Language Models in Natural Language Understanding Page 1 110
COMMUNICATIONS OF THE ACM | JANUARY 2024 | VOL. 67 | NO. 1 research IMA GE B Y …

Despite significant progress in text generation models, a serious limitation is their tendency
to produce text that is factually inconsistent with information in the input. Recent work has …

Recent studies indicate that NLU models are prone to rely on shortcut features for prediction,
without achieving true language understanding. As a result, these models fail to generalize …

Recently, NLP models have achieved remarkable progress across a variety of tasks;
however, they have also been criticized for being not robust. Many robustness problems can …

Recently proposed BERT-based evaluation metrics for text generation perform well on
standard benchmarks but are vulnerable to adversarial attacks, eg, relating to information …

Neural NLP models tend to rely on spurious correlations between labels and input features
to perform their tasks. Minority examples, ie, examples that contradict the spurious …

Gender-bias stereotypes have recently raised significant ethical concerns in natural
language processing. However, progress in detection and evaluation of gender bias in …

Natural language inference (NLI) models are susceptible to learning shortcuts, ie decision
rules that spuriously correlate with the label. As a result, they achieve high in-distribution …