  As language models such as GPT-3 become increasingly successful at generating
realistic text, questions about what purely text-based modeling can learn about
the world have become more urgent. Is text purely syntactic, as skeptics argue?
Or does it in fact contain some semantic information that a sufficiently
sophisticated language model could use to learn about the world without any
additional inputs? This paper describes a new model that suggests some
qualified answers to those questions. By theorizing the relationship between
text and the world it describes as an equilibrium relationship between a
thermodynamic system and a much larger reservoir, this paper argues that even
very simple language models do learn structural facts about the world, while
also proposing relatively precise limits on the nature and extent of those
facts. This perspective promises not only to answer questions about what
language models actually learn, but also to explain the consistent and
surprising success of cooccurrence prediction as a meaning-making strategy in
AI.
