Large language models and linguistic intentionality

Grindrod, J. ORCID: https://orcid.org/0000-0001-8684-974X (2024) Large language models and linguistic intentionality. Synthese, 204. 71. ISSN 1573-0964 doi: 10.1007/s11229-024-04723-8

Abstract/Summary

Do large language models like Chat-GPT or Claude 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 that LLM outputs are meaningful.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/117589
Identification Number/DOI	10.1007/s11229-024-04723-8
Refereed	Yes
Divisions	Arts, Humanities and Social Science > School of Humanities > Philosophy
Publisher	Springer
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