Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

We survey a current, heated debate in the artificial intelligence (AI) research community on
whether large pretrained language models can be said to understand language—and the …

Pretrained Language Models (LMs) have demonstrated ability to perform numerical
reasoning by extrapolating from a few examples in few-shot settings. However, the extent to …

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often
rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We …

Natural language processing models often exploit spurious correlations between task-
independent features and labels in datasets to perform well only within the distributions they …

Controlled text perturbation is useful for evaluating and improving model generalizability.
However, current techniques rely on training a model for every target perturbation, which is …

NLP community is currently investing a lot more research and resources into development of
deep learning models than training data. While we have made a lot of progress, it is now …