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The conclusion here seems improbable at best -- if I understand it right, the assumption is that somewhere in the training data is the literal token string (wil
by andrewla 2y ago
The conclusion here seems improbable at best -- if I understand it right, the assumption is that somewhere in the training data is the literal token string (wild)(cat)[other tokens](w)(i)(l)(d)(c)(a)(t)?
Even a transformer trained exclusively on examples of the form (token)(token)(letter-token)(letter-token)...(letter-token) where the letter-tokens are single letters and the tokens represent the standard tokenizer output would have trouble performing this task.
I guess this last statement is testable. I suspect that it would be unsuccessful without vast amounts of training data of this form, and I think we can probably agree that although there may be some, there are not sufficient examples of this form in standard LLM training sets to be able to learn this task specifically; the ability to do this (limited as it is) is an emergent capability of general-purpose LLMs.
- HarHarVeryFunny 2y agoWhat I'm saying is that: 1) Novel words are handled because they are just sequences of common tokens 2) Token -> letter sequence associations are either: a) Deliberately added to the training set, and/or b) Naturally occurring in the training set, which due to sheer size almost inevitably contains many, many, examples of word to letter sequence associations Given how models used to fail badly on tasks related to this, and now do much better, it's quite likely that model providers have simply added these to the training set, just as they have added data to improve other benchmark tests. That said, what I was pointing out is that words are represented as token sequences, so a word spelling sample is effectively a seq-2-seq (tokens to letters) sample, and we'd expect the model (which is built for seq-2-seq!) to be able to easily learn and generalize over these.