> The results we obtained in Section 7 imply that, at least on simple datasets like TinyStories and Wikipedia, LLM predictions contain much quantifiable structure insofar that they often can be described in terms of our simple statistical rules

> we find that for 79% and 68% of LLM next-token distributions on TinyStories and Wikipedia, respectively, their top-1 predictions agree with those provided by our N-gram rulesets

Two prediction methods may have completely different mechanisms, but agree sometimes, because they are both predicting the same thing.

Seems a fairly large proportion of language can be predicted by a simpler model.. But it's the remaining percent that's the difficult part; which simple `n-gram` models are bad at, and transformers are really good at.