Transformer large language models (LLMs) perform exceptionally well across a variety of
linguistic tasks. These models represent relationships between words in a sentence via …

Understanding how sentences are internally represented in the human brain, as well as in
large language models (LLMs) such as ChatGPT, is a major challenge for cognitive science …

Given the remarkable capabilities of large language models (LLMs), there has been a
growing interest in evaluating their similarity to the human brain. One approach towards …

Are the predictions of humans and language models affected by similar things? Research
suggests that while comprehending language, humans make predictions about upcoming …

Large Language Models (LLMs), which match or exceed human performance on many
linguistic tasks, are nonetheless commonly criticized for not “understanding” language …

Psycholinguistic research suggests that humans may build a representation of linguistic
input that is' good-enough'for the task at hand. This study examines what architectural …

Piecing together the meaning of a narrative requires understanding not only the individual
words but also the intricate relationships between them. How does the brain construct this …

According to the parallel architecture, syntactic and semantic information processing are two
separate streams that interact selectively during language comprehension. While …

Humans use complex linguistic structures to transmit ideas to one another. The brain is
thought to deploy specialized computations to process these structures. Recently, a new …

In this thesis, we investigate several approaches to evaluate modern neural language
models through the lens of systematicity, in order to assess their human-level reasoning and …