alt Hacker NewsThose are questions that are sidestepped with simply a different input paradigm than BPE tokenization. See the Byte Latent Transformer - https://arxiv.org/pdf/2412.09871 - where a similar scale byte latent model trained on the same dataset >>> a vanilla transformer on word and character manipulation tasks.
For example, Llama 3 trained on 1T tokens scores 1.1% on a CUTE spelling benchamrk, while the equivalent byte latent equivalent trained on the same dataset scores 99.9%. Another example is 0.4% vs 48.7% on a Substitute Char benchmark.
It all falls down to the same thing. Researchers are not optimizing for passing as a human.
So, sure, we can special plead the Turing test out of the picture. But if we don’t propose an alternative to take its place, we’re left right back at the same silly situation that the top level commenter was observing and that Turing was trying to move away from: enmired in a useless, meaningless argument about semantics.