Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

More than one hundred benchmarks have been developed to test the commonsense
knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems …

Negation has been shown to be a major bottleneck for masked language models, such as
BERT. However, whether this finding still holds for larger-sized auto-regressive language …

Recent studies demonstrated that large language models (LLMs) can excel in many tasks
via in-context learning (ICL). However, recent works show that ICL-prompted models tend to …

Although large language models (LLMs) have apparently acquired a certain level of
grammatical knowledge and the ability to make generalizations, they fail to interpret …

Recently, large language models (LLMs), including notable models such as GPT-4 and
burgeoning community models, have showcased significant general language …

Negation poses a challenge in many natural language understanding tasks. Inspired by the
fact that understanding a negated statement often requires humans to infer affirmative …

The NLP community typically relies on performance of a model on a held-out test set to
assess generalization. Performance drops observed in datasets outside of official test sets …

We introduce an evaluation methodology for reading comprehension tasks based on the
intuition that certain examples, by the virtue of their linguistic complexity, consistently yield …

The NLP community typically relies on performance of a model on a held-out test set to
assess generalization. Performance drops observed in datasets outside of official test sets …