The rise and success of large language models undermines virtually every strong claim for
the innateness of language that has been proposed by generative linguistics. Modern …

To what degree can language be acquired from linguistic input alone? This question has
vexed scholars for millennia and is still a major focus of debate in the cognitive science of …

Since language models are used to model a wide variety of languages, it is natural to ask
whether the neural architectures used for the task have inductive biases towards modeling …

We propose an alternate approach to quantifying how well language models learn natural
language: we ask how well they match the statistical tendencies of natural language. To …

There is much debate over the degree to which language learning is governed by innate
language-specific biases, or acquired through cognition-general principles. Here we …

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

There are many questions one can ask about human language: its distinctive properties,
neural representation, characteristic uses including use in communicative contexts …

We design experiments to study $\textit {how} $ generative language models, like GPT, learn
context-free grammars (CFGs)--diverse language systems with a tree-like structure capturing …

Large language models (LLMs) represent a major advance in artificial intelligence and, in
particular, toward the goal of human-like artificial general intelligence. It is sometimes …

Inducing a grammar from text has proven to be a notoriously challenging learning task
despite decades of research. The primary reason for its difficulty is that in order to induce …