  Concepts play a pivotal role in various human cognitive functions, including
learning, reasoning and communication. However, there is very little work on
endowing machines with the ability to form and reason with concepts. In
particular, state-of-the-art large language models (LLMs) work at the level of
tokens, not concepts.
  In this work, we analyze how well contemporary LLMs capture human concepts
and their structure. We then discuss ways to develop concept-aware LLMs, taking
place at different stages of the pipeline. We sketch a method for pretraining
LLMs using concepts, and also explore the simpler approach that uses the output
of existing LLMs. Despite its simplicity, our proof-of-concept is shown to
better match human intuition, as well as improve the robustness of predictions.
These preliminary results underscore the promise of concept-aware LLMs.
