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 …

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 …

This paper investigates the ability of artificial neural networks to judge the grammatical
acceptability of a sentence, with the goal of testing their linguistic competence. We introduce …

This paper provides an overview of what we take to be the key current issues in the field of
Generative Grammar, the study of the human Faculty of Language. We discuss some of the …

Rapid progress in machine learning for natural language processing has the potential to
transform debates about how humans learn language. However, the learning environments …

This paper explores the knowledge of linguistic structure learned by large artificial neural
networks, trained via self-supervision, whereby the model simply tries to predict a masked …

An investigation of how children balance rules and exceptions when they learn languages.
All languages have exceptions alongside overarching rules and regularities. How does a …

Language is an essential part of what makes us human. Where did it come from? How did it
develop into the complex system we know today? And what can an evolutionary perspective …

With the crystallization of the “generative enterprise” half a century ago, two concepts
became salient: the initial state and final states of the language faculty, respectively, UG (the …

Columbia Themes in Philosophy is a new series with a broad and accomodating thematic
reach as well as an ecumenical approach to the outdated disjunction between analytic and …