Humans can reason compositionally when presented with new tasks. Previous research
shows that appropriate prompting techniques enable large language models (LLMs) to …

Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL)
ability, where the model learns to do an unseen task simply by conditioning on a prompt …

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

Neural models greatly outperform grammar-based models across many tasks in modern
computational linguistics. This raises the question of whether linguistic principles, such as …

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 …

Human linguistic capacity is often characterized by compositionality and the generalization it
enables--human learners can produce and comprehend novel complex expressions by …

Deep learning has enabled major advances across most areas of artificial intelligence
research. This remarkable progress extends beyond mere engineering achievements and …

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

While large language models (LLMs) bring not only performance but also complexity, recent
work has started to turn LLMs into data generators rather than task inferencers, where …