There should be somewhere in the corpus, "the is spelled t h e" that this system can use to pull this out. We can ask gpt to spell out individual words in NATO phonetic and see how it does.
> There should be somewhere in the corpus, "the is spelled t h e" that this system can use to pull this out.
Such an approach would require an enormous table, containing all written words, including first and last names, and would still fail for made up words.
A more tractable approach would be to give it the map between the individual tokens and their letter component, but then you have the problem that this matching depends on the specific encoding used by the model (it varies between models). You could give it to the model during fine-tuning though.
They cannot “figure” it, they could learn it but for that it would need to be in it's training data (which isn't because nobody is writing down the actual pairing in every byte pair encoding in plain text. Also the LLM has no clue about what encoding it uses unless you tell it somehow in the fine-tuning process or the prompt.)
It's as feasible as telling how many chars in html lead to this comment by looking at a screenshot. LLM doesn't see characters, tokens, numbers or its own activations. LLM is a "set of rules" component in a chinese room scenario. Anything an operator of that room does is lower-level.
GGP's idea suggests that an LLM, allegedly as a whole-room, receives something like: "hey, look at these tokens: <tokens>, please infer the continuation". This puts it into a nested-room's-operator position, which (1) it is not, (2) there's no nested room.
{the:1, t: 2, h:3, e:4}
There should be somewhere in the corpus, "the is spelled t h e" that this system can use to pull this out. We can ask gpt to spell out individual words in NATO phonetic and see how it does.