Recent natural language processing (NLP) techniques have accomplished high
performance on benchmark data sets, primarily due to the significant improvement in the …

Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing and computer vision. Unlike traditional neural …

GPT-3 can perform numerous tasks when provided a natural language prompt that contains
a few training examples. We show that this type of few-shot learning can be unstable: the …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

Transformer-based models have pushed state of the art in many areas of NLP, but our
understanding of what is behind their success is still limited. This paper is the first survey of …

Natural language processing tasks, such as question answering, machine translation,
reading comprehension, and summarization, are typically approached with supervised …

Recent success of pre-trained language models (LMs) has spurred widespread interest in
the language capabilities that they possess. However, efforts to understand whether LM …

With the emergence of foundation models (FMs) that are trained on large amounts of data at
scale and adaptable to a wide range of downstream applications, AI is experiencing a …

To avoid giving wrong answers, question answering (QA) models need to know when to
abstain from answering. Moreover, users often ask questions that diverge from the model's …

We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and
evaluate the ability of question-answering systems to generalize to new data. Our first test …