  Do large language models like Chat-GPT or LLaMa meaningfully use the words
they produce? Or are they merely clever prediction machines, simulating
language use by producing statistically plausible text? There have already been
some initial attempts to answer this question by showing that these models meet
the criteria for entering meaningful states according to metasemantic theories
of mental content. In this paper, I will argue for a different approach - that
we should instead consider whether language models meet the criteria given by
our best metasemantic theories of linguistic content. In that vein, I will
illustrate how this can be done by applying two such theories to the case of
language models: Gareth Evans' (1982) account of naming practices and Ruth
Millikan's (1984, 2004, 2005) teleosemantics. In doing so, I will argue that it
is a mistake to think that the failure of LLMs to meet plausible conditions for
mental intentionality thereby renders their outputs meaningless, and that a
distinguishing feature of linguistic intentionality - dependency on a
pre-existing linguistic system - allows for the plausible result LLM outputs
are meaningful.
