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Notes -
Unfortunately, it's a dumber problem than that. Neural nets can pick up a lot of very surprising things from their source data. StableDiffusion can pick up artists and connotations that aren't obvious from its input data, and GPT is starting to 'learn' some limited math despite not being taught what the underlying mathematical symbols are (albeit with some often-sharp limitations). GPT does actually have a near-encyclopedic knowledge of IPA pronunciation, and you can easily prompt it to rewrite whole sentences in phonetic pronunciation. And we're not talking a situation where these programs try to do something rhyme-like and fail, like match up words with large number of letter overlaps without understanding pronunciation. Indeed, one of the limited ways people have successfully gotten rhymes out of it have involved prompting it to explain the pronunciation first. (Though not that this runs into and very quickly fills up the available Attention.) Instead, GPT and GPT-like approaches struggle to rhyme even when trained on a corpus of poetry or limericks: the information is in the training data, it's just inaccessible at the scope the model is working at : either it does transparent copy or it doesn't get very close.
Gwern makes the credible argument that (at least part of) GPT's problem is that it works in fairly weird byte-pair encodings to avoid hitting some of those massively diminishing returns as early as had it been trained on phonetic or character-level minimum units, but at the cost of completely eliminating the ability to handle or even examine certain sub-encoding concepts. It's possible that we'll eventually get enough input data and parameters to just break these limits from an unintuitive angle, but the split from how we suspect human brains handle things may just mean that this scope of BPEs cause bad results in this field and a better work-around needs to be designed (at least where you need these concepts to be examined).
((Other tools using a similar tokenizer have similar constraints.))
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