How do humans learn language, and can the first language be learned at all? These
fundamental questions are still hotly debated. In contemporary linguistics, there are two …

Artificial neural networks have emerged as computationally plausible models of human
language processing. A major criticism of these models is that the amount of training data …

Recurrent neural networks have been very successful at predicting sequences of words in
tasks such as language modeling. However, all such models are based on the conventional …

The training objective of unidirectional language models (LMs) is similar to a
psycholinguistic benchmark known as the cloze task, which measures next-word …

Language models (LMs) are statistical models trained to assign probability to human-
generated text. As such, it is reasonable to question whether they approximate linguistic …

Neural language models predict the next token using a latent representation of the
immediate token history. Recently, various methods for augmenting neural language models …

Artificial Intelligence applications show great potential for language-related tasks that rely on
next-word prediction. The current generation of large language models have been linked to …

During language acquisition, children follow a typical sequence of learning stages, whereby
they first learn to categorize phonemes before they develop their lexicon and eventually …

Understanding how language and linguistic constructions are processed in the brain is a
fundamental question in cognitive computational neuroscience. In this study, we explore the …

Vocabulary growth and syntactic development are known to be highly correlated in early
child language. What determines when words are acquired and how can this help us …