Comment by D-Machine
18 days ago
An LLM has an internal linguistic model (i.e. it knows token patterns), and that linguistic model models humans' linguistic models (a stream of tokens) of their actual world models (which involve far, far more than linguistics and tokens, such as logical relations beyond mere semantic relations, sensory representations like imagery and sounds, and, yes, words and concepts).
So LLMs are linguistic (token pattern) models of linguistic models (streams of tokens) describing world models (more than tokens).
It thus does not in fact follow that LLMs model the world (as they are missing everything that is not encoded in non-linguistic semantics).
At this point, anyone claiming that LLMs are "just" language models aren't arguing in good faith. LLMs are a general purpose computing paradigm. LLMs are circuit builders, the converged parameters define pathways through the architecture that pick out specific programs. Or as Karpathy puts it, LLMs are a differentiable computer[1]. Training LLMs discovers programs that well reproduce the input sequence. Tokens can represent anything, not just words. Roughly the same architecture can generate passable images, music, or even video.
[1] https://x.com/karpathy/status/1582807367988654081
If it's an LLM it's a (large) language model. If you use ideas from LLM architecture in other non-language models, they are not language models.
But it is extremely silly to say that "large language models are language models" is a bad faith argument.
No, its extremely silly to use the incidental name of a thing as an argument for the limits of its relevance. LLMs were designed to model language, but that does not determine the range of their applicability, or even the class of problems they are most suited for. It turns out that LLMs are a general computing architecture. What they were originally designed for is incidental. Any argument that starts off "but they are language models" is specious out of the gate.
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