  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 claims about human-like
linguistic performance and their applications are hailed both as a key step
towards Artificial General Intelligence and as major advance in understanding
the cognitive, and even neural basis of human language. We analyze the
contribution of large language models as theoretically informative
representations of a target system vs. atheoretical powerful mechanistic tools,
and we identify the key abilities that are still missing from the current state
of development and exploitation of these models.
