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 …

The widespread adoption of large language models (LLMs) makes it important to recognize
their strengths and limitations. We argue that in order to develop a holistic understanding of …

The ability to derive underlying principles from a handful of observations and then
generalize to novel situations--known as inductive reasoning--is central to human …

In tasks like semantic parsing, instruction following, and question answering, standard deep
networks fail to generalize compositionally from small datasets. Many existing approaches …

Compositional generalization--understanding unseen combinations of seen primitives--is an
essential reasoning capability in human intelligence. The AI community mainly studies this …

Compositional generalization benchmarks for semantic parsing seek to assess whether
models can accurately compute meanings for novel sentences, but operationalize this in …

Though modern neural networks have achieved impressive performance in both vision and
language tasks, we know little about the functions that they implement. One possibility is that …

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

A characteristic feature of human semantic cognition is its ability to not only store and
retrieve the properties of concepts observed through experience, but to also facilitate the …

Targeted syntactic evaluations of language models ask whether models show stable
preferences for syntactically acceptable content over minimal-pair unacceptable inputs. Most …