Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Multitask prompted finetuning (MTF) has been shown to help large language models
generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Recent breakthroughs in natural language processing (NLP) have permitted the synthesis
and comprehension of coherent text in an open-ended way, therefore translating the …

As NLP models achieved state-of-the-art performances over benchmarks and gained wide
applications, it has been increasingly important to ensure the safe deployment of these …

Designing robust text-to-image (T2I) models have been extensively explored in recent years,
especially with the emergence of diffusion models, which achieves state-of-the-art results on …

Design biases in NLP systems, such as performance differences for different populations,
often stem from their creator's positionality, ie, views and lived experiences shaped by …

Misinformation threatens modern society by promoting distrust in science, changing
narratives in public health, heightening social polarization, and disrupting democratic …

Recent breakthroughs in large language models (LLMs) have centered around a handful of
data-rich languages. What does it take to broaden access to breakthroughs beyond first …