Year: 2024
Type: article
Abstract: This dissertation is a collection of three independent but thematically connected papers on language models. The first assesses a currently popular account of the nature of the linguistic competence ostensibly manifested by LMs. According to this account LMs are “stochastic parrots”, whose linguistic outputs, though they look outwardly meaningful, are actually only cleverly stitched together bits and pieces of language which have no meaning at all except accidentally. This claim depends on what I call the Communicative Intention argument. In this chapter I demonstrate that the argument is largely driven by strong Gricean assumptions about the nature of linguistic competence that I argue are now known to be empirically untenable. Moreover, I show that once we give up these assumptions and fairly assess the architecture of both LLM and human cognitive architectures, we will find no good reasons to suppose large language models lack communicative intentions. The second paper addresses the larger debate in the context of which the Communicative Intention argument is made: the debate about meaning in LMs. I argue here that there are two senses of “meaning” in the debate, which I term semantic meaning and content meaning. A failure to distinguish between these two varieties of meaning has led to many of the characteristic confusions in the debate. The final paper deals with metalinguistic capacities in LMs. This paper introduces a novel way of accounting for any ostensible differences in the competences manifested by humans and LMs. The account has to do with what I call metalinguistic agency — roughly, the claim is that the agential structure of the linguistic competences in LMs and humans differ; any ostensible differences between them can be accounted for in terms of this difference in agential structure.
Author: Osman Attah, Nuhu
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7034229882
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