Research: LLM & Human Language Use

http://philsci-archive.pitt.edu/21983/

Against AI Understanding and Sentience: Large Language Models, Meaning, and
the Patterns of Human Language Use

Abstract
Large language models such as ChatGPT are deep learning architectures
trained on immense quantities of text. Their capabilities of producing
human-like text are often attributed either to mental capacities or the
modeling of such capacities. This paper argues, to the contrary, that
because much of meaning is embedded in common patterns of language use,
LLMs can model the statistical contours of these usage patterns. We agree
with distributional semantics that the statistical relations of a text
corpus reflect meaning, but only part of it. Written words are only one
part of language use, although an important one as it scaffolds our
interactions and mental life. In human language production, preconscious
anticipatory processes interact with conscious experience. Human language
use constitutes and makes use of given patterns and at the same time
constantly rearranges them in a way we compare to the creation of a
collage. LLMs do not model sentience or other mental capacities of humans
but the common patterns in public language use, clichés and biases
included. They thereby highlight the surprising extent to which human
language use gives rise to and is guided by patterns.