As far as I know, that's not the case. The tokenizer takes a bunch of characters, like "berry", identifies it as a token, and what the LLM gets is the token ID. It doesn't have access to the information about which letters that token is composed of. Here is an explanation by OpenAI themselves:
https://help.openai.com/en/articles/4936856-what-are-tokens-... - as you can see, "Models take the prompt, convert the input into a list of tokens, processes the prompt, and convert the predicted tokens back to the words we see in the response". And the tokens are basically IDs, without any internal structure - there are examples there.
If I'm missing something and you have a source for the claim that character information is present in the input after tokenization, please provide it. I have never implemented an LLM or fiddled with them at low level so I might be missing some detail, but from everything I have read, I'm pretty sure it doesn't work that way.
