The Outputs of Large Language Models are Meaningless
This addresses a foundational philosophical problem in AI and linguistics regarding the nature of meaning and understanding in machine-generated text.
The paper argues that large language model outputs are meaningless because they lack the necessary intentions for literal meaning, and defends this view against semantic externalist and internalist counterarguments.
In this paper, we offer a simple argument for the conclusion that the outputs of large language models (LLMs) are meaningless. Our argument is based on two key premises: (a) that certain kinds of intentions are needed in order for LLMs' outputs to have literal meanings, and (b) that LLMs cannot plausibly have the right kinds of intentions. We defend this argument from various types of responses, for example, the semantic externalist argument that deference can be assumed to take the place of intentions and the semantic internalist argument that meanings can be defined purely in terms of intrinsic relations between concepts, such as conceptual roles. We conclude the paper by discussing why, even if our argument is sound, the outputs of LLMs nevertheless seem meaningful and can be used to acquire true beliefs and even knowledge.