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 …

As research in large language models (LLMs) continues to accelerate, LLM-based
evaluation has emerged as a scalable and cost-effective alternative to human evaluations …

Large language models (LLMs) have been widely studied for their ability to store and utilize
positive knowledge. However, negative knowledge, such as" lions don't live in the ocean", is …

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 …

The increasing importance and proliferation of text data provide a unique opportunity and
novel lens to study human communication across a myriad of business and marketing …

A number of recent benchmarks seek to assess how well models handle natural language
negation. However, these benchmarks lack the controlled example paradigms that would …

Recent work has shown that deep learning models in NLP are highly sensitive to low-level
correlations between simple features and specific output labels, leading to overfitting and …

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

Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on
model adaptation, necessitating more efficient alternatives to conventional fine-tuning. Given …

Negation is poorly captured by current language models, although the extent of this problem
is not widely understood. We introduce a natural language inference (NLI) test suite to …