[SpicyLemonZest]

"Understanding" is your word, it doesn't appear in the source article nor the comment you're responding to. The Chinese Room argument does not attempt to show that a mapping from input to output can't implement a latent space, or that it can't implement complex models of what the language is describing. If a person can express those things explicitly in the output, or if you have to do them in order to correctly respond how a person would, then the room by definition has those capabilities.

(What's the point of the argument if it doesn't tell us anything about the capabilities or internals of an AI? I'm not sure.)

[jacobr1]

Right - the point I was refuting is that LLMs are "just statistical models of words." More is going on. Does that imply "understanding?" I don't know, I'm not sure we have a good enough definition to say. But it does mean that the models are more complex that say, markov chain graphs with corpus frequencies. It seems we are encoding data in the latent space with much higher complexity than "just words." There is higher order semantic information being captured - probably not the same has human "thoughts" - but again - also not _just words_.