It has long been argued that only humans could produce and understand language. But
now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the …

Modern machine learning has subverted and bypassed the theoretical framework of
Chomsky's generative approach to linguistics, including its core claims to particular insights …

Children can acquire language from less than 100 million words of input. Large language
models are far less data-efficient: they typically require 3 or 4 orders of magnitude more data …

Scientific experimentation involves an iterative process of creating hypotheses, designing
experiments, running experiments, and analyzing the results. Can we build AI research …

This chapter critically examines the potential contributions of modern language models to
theoretical linguistics. Despite their focus on engineering goals, these models' ability to …

The increasing versatility of language models LMs has given rise to a new class of
benchmarks that comprehensively assess a broad range of capabilities. Such benchmarks …

The staggering pace with which the capabilities of large language models (LLMs) are
increasing, as measured by a range of commonly used natural language understanding …

We present our proposed solution to the BabyLM challenge [arXiv: 2301.11796], whose goal
was to improve the sample efficiency of language models. We trained an ensemble …

Large Language Models (LLMs) are capable of displaying a wide range of abilities that are
not directly connected with the task for which they are trained: predicting the next words of …

Chomsky and others have very directly claimed that large language models (LLMs) are
equally capable of learning languages that are possible and impossible for humans to learn …