  Much of the recent discourse within the ML community has been centered around
Large Language Models (LLMs), their functionality and potential -- yet not only
do we not have a working definition of LLMs, but much of this discourse relies
on claims and assumptions that are worth re-examining. We contribute a
definition of LLMs, critically examine five common claims regarding their
properties (including 'emergent properties'), and conclude with suggestions for
future research directions and their framing.
