Large language models and linguistic intentionality
This addresses a foundational philosophical problem in AI and linguistics regarding the nature of meaning in LLMs, but it is incremental as it builds on existing metasemantic theories without introducing new empirical methods or data.
The paper tackles the question of whether large language models (LLMs) produce meaningful language by arguing that evaluating them based on linguistic intentionality, rather than mental intentionality, is more appropriate. It applies metasemantic theories like those of Evans and Millikan to conclude that LLM outputs can be meaningful due to their dependency on pre-existing linguistic systems.
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.