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 …

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

We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a
developmentally plausible corpus. This shared task is intended for participants with an …

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 …

Large language models (LLMs) and LLM-driven chatbots such as ChatGPT have shown
remarkable capacities in comprehending and producing language. However, their internal …

When acquiring syntax, children consistently choose hierarchical rules over competing non-
hierarchical possibilities. Is this preference due to a learning bias for hierarchical structure …

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 …

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

In a recent manuscript entitled “Modern language models refute Chomsky's approach to
language”, Steven Piantadosi proposes that large language models such as GPT-3 can …