The ByT5 paper shows significantly better resiliency to typos and noise in internet scale benchmarks, as well as better reasoning capabilities with tasks like punctuation and spacing correction: https://arxiv.org/pdf/2105.13626.pdf

The analogy doesn’t hold because while you might not parse specific characters by default, you are trained on the character level and you can switch to that parsing mode. This simply is not possible for LLMs without hacks like inserting spaces, which then suffers from poor performance due to this being rare in pretraining.

The reason byte level models aren’t as popular is because inference speed is significantly slower for a given completion length, and training for the same context window in characters is significantly more expensive

I say this as someone who’s deployed ByT5 vs normal T5 in prod, having found this to be true in real world use cases. No reasonable amount of fine tuning can save GPT from performing poorly on a space insertion task (“WHOLEFDSMKT” -> “Whole Foods Market”), while ByT5 just works.