Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

We present a novel task and dataset for evaluating the ability of vision and language models
to conduct visio-linguistic compositional reasoning, which we call Winoground. Given two …

Abstract The Winograd Schema Challenge—a set of twin sentences involving pronoun
reference disambiguation that seem to require the use of commonsense knowledge—was …

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained
end-to-end to solve logical reasoning problems presented in natural language? We attempt …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Word co‐occurrence patterns in language corpora contain a surprising amount of
conceptual knowledge. Large language models (LLMs), trained to predict words in context …

Language models (LMs) trained on large amounts of data have shown impressive
performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to …

Commonsense knowledge is essential for many AI applications, including those in natural
language processing, visual processing, and planning. Consequently, many sources that …

Large amounts of training data are one of the major reasons for the high performance of
state-of-the-art NLP models. But what exactly in the training data causes a model to make a …

Commonsense knowledge acquisition and reasoning have long been a core artificial
intelligence problem. However, in the past, there has been a lack of scalable methods to …