In principle languages are arbitrary, but in practice they’re describing the same world and the same concepts end up being useful.
Yes, if you construct an embedding vector on texts-A, and another on texts-B where (A, B) are translations of each other, then an "unsupervised" algorithm really will give you the dizzying heights of a little above coin-flip accuracy on highly engineered self-selected benchmarks.
They evaluate by by looking at the most in-use words in each vocab.. so you take the most in-use words on translations of Wikipedia, whose frequency is decided by the need of translation.. and then you use that to evaluate.
It is blindingly obvious that the structure and frequency of heirglphys on tombs, Chinese glyphs in poetry, and latin in medieval liturgical literature are not distributed by Reality.. written in this order by God so that the Langauge of Reality is what places "d" alongside "oor". We already know this to be the case. The assumption of its opposite is rank pseudoscience.
I mean, on the first level, of course they're entirely determined by reality in the sense that the human brain is a real, physical object. But also on a second level they're still entirely determined by reality because the shape of the human mind is also entirely determined by reality. What is the mind for except reflecting reality? What is language for except communicating it? Sure that reality is warped, filtered, reduced and biased, but the data is still in there. That's in large part why LLMs need such ludicrously large training runs.
I don't think letter frequency is objectively determined, but we know for a fact (many studies!) that the features that large language models learn are far, far above the scale of letters. Even arguing about phrases isn't engaging with the current state of the art.
We're not talking about Markov chains here.