  The widespread success of large language models (LLMs) has been met with
skepticism that they possess anything like human concepts or meanings. Contrary
to claims that LLMs possess no meaning whatsoever, we argue that they likely
capture important aspects of meaning, and moreover work in a way that
approximates a compelling account of human cognition in which meaning arises
from conceptual role. Because conceptual role is defined by the relationships
between internal representational states, meaning cannot be determined from a
model's architecture, training data, or objective function, but only by
examination of how its internal states relate to each other. This approach may
clarify why and how LLMs are so successful and suggest how they can be made
more human-like.
