Do Language Models' Words Refer?
This addresses a foundational philosophical question about AI meaning for researchers in linguistics and AI, but it is incremental as it builds on existing externalist theories.
The paper tackles the problem of whether language models' words can refer to the real world, arguing that despite their lack of direct interaction, the natural histories of their training text may enable referential connections.
What do language models (LMs) do with language? Everyone agrees that they can produce sequences of (mostly) coherent strings of English. But do those sentences mean something, or are LMs simply babbling in a convincing simulacrum of language use? Here we will address one aspect of this broad question: whether LMs' words can refer, that is, achieve "word-to-world" connections. There is prima facie reason to think they do not since LMs do not interact with the world in the way that ordinary language users do. Drawing on insights from the externalist tradition in philosophy of language, we argue that those appearances are misleading: even if the inputs to an LM are simply strings of text, they are strings of text with natural histories, and that may suffice to put LMs' words into referential contact with the external world.