> Possibly I'm not understanding you here. Supposing that certain meanings were intrinsic properties, would the relationships between those concepts not also carry meaning? Can't intrinsic things also be used as building blocks? And why would we expect an ML model to be incapable of learning both of those things? Why should encoding semantics though spatial adjacency be mutually exclusive with the processing of intrinsic concepts? (Hopefully I'm not betraying some sort of great ignorance here.)
I probably shouldn't respond to this part, because I don't really agree with the original assertion. Or rather, I think this ends up boiling down to a disagreement over semantics, and so isn't a particularly interesting question.
Relationships between concepts covers a lot of what "meaning" is. You can teach a computer to translate from language X to Y purely based on it learning the relationships of words to each other in each language, and then generating a mapping between the weight-graph of X to the weight-graph of Y. (Yeah, citation needed; I remember reading some specific evidence for this, but I don't remember where.) So you can get a long way with just relationships.
At the same time, I don't think that proves that the relationships between concepts are everything. A human getting burned and learning the word "hot" could be described as "hot" having an intrinsic meaning. But you could equally describe it as a relationship between the action taken, the sensation experienced, and the phonemes heard. If all those are "concepts", then the relationships between concepts are everything. If they're not, then you can call something intrinsic. Personally, that strikes me as a pointless philosophical question.
I guess you could argue that if you have an LLM trained on mostly English but also enough Chinese to be able to translate, and it generates text including the word "hot", then if you compare that to the same LLM generating text including the Chinese word for hot, that there's more opportunity for drift in the Chinese output. The first case has the chain of a human feeling pain => writing text containing "hot" => generating text containing "hot", whereas the second has the chain of a human feeling pain => writing text containing "hot" => encoded associations between English and Chinese concepts embedded in weights => writing Chinese text containing Chinese "hot". The English "hot" output is more tightly connected to and more directly derives from the physical sensation of burning. (This is of course assuming majority English training data, and in particular a relative lack of Chinese training data containing the "hot" word/concept.) So in a way, you could claim that the question of whether the word "hot" has an intrinsic meaning is relevant and useful. But it seems to me that's just one way of describing the origins of training data; use it if it's useful.