>> In fact many propose that when you train an LLM, in order to be able to predict the next word with enough accuracy, it must internally build a world model.

>> In fact many propose that when you train an LLM, in order to be able to predict the next word with enough accuracy, it must internally build a world model. Oh, but it does build a world model. Only, its "world", is a gigantic table of token collocations, and their probabilities. So, for example, it can tell you with great accuracy that "king - man + woman = queen", but that's the only way it can map "king" to something else: by moving around its embedding space, I guess. Unfortunately, if you can only map between tokens, when you have no representation of the meaning of those tokens, other than more tokens of which you don't have any other representation, well, then, any mapping you can build won't really help you understand what those tokens mean. If only we could find a way to map tokens to real-world entities, or to some kind of representation of ... things... outside of token space. (yes yes, the frame problem, old as AI)