  Language Models (LMs) have achieved impressive performance on various
linguistic tasks, but their relationship to human language processing in the
brain remains unclear. This paper examines the gaps and overlaps between LMs
and the brain at different levels of analysis, emphasizing the importance of
looking beyond input-output behavior to examine and compare the internal
processes of these systems. We discuss how insights from neuroscience, such as
sparsity, modularity, internal states, and interactive learning, can inform the
development of more biologically plausible language models. Furthermore, we
explore the role of scaling laws in bridging the gap between LMs and human
cognition, highlighting the need for efficiency constraints analogous to those
in biological systems. By developing LMs that more closely mimic brain
function, we aim to advance both artificial intelligence and our understanding
of human cognition.
