Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding
Stevan Harnad
Apart from what (little) OpenAI may be concealing from us, we all know
(roughly) how ChatGPT works (its huge text database, its statistics, its vector
representations, and their huge number of parameters, its next-word training,
and so on). But none of us can say (hand on heart) that we are not surprised by
what ChatGPT has proved to be able to do with these resources. This has even
driven some of us to conclude that ChatGPT actually understands. It is not true
that it understands. But it is also not true that we understand how it can do
what it can do. I will suggest some hunches about benign biases: convergent
constraints that emerge at LLM scale that may be helping ChatGPT do so much
better than we would have expected. These biases are inherent in the nature of
language itself, at LLM scale, and they are closely linked to what it is that
ChatGPT lacks, which is direct sensorimotor grounding to connect its words to
their referents and its propositions to their meanings. These convergent biases
are related to (1) the parasitism of indirect verbal grounding on direct
sensorimotor grounding, (2) the circularity of verbal definition, (3) the
mirroring of language production and comprehension, (4) iconicity in
propositions at LLM scale, (5) computational counterparts of human categorical
perception in category learning by neural nets, and perhaps also (6) a
conjecture by Chomsky about the laws of thought. The exposition will be in the
form of a dialogue with ChatGPT-4.