It already knows which words are, statistically, more commonly rhymed with each other. From the massive list of training poems. This is what the massive data sets are for. One of the interesting things is that it’s not predicting backwards, exactly. It’s actually mathematically converging on the response text to the prompt, all the words at the same time.
We also check to see if the word that popped into our heads actually rhymes by saying it out loud. Actual validation steps we can take is a bigger difference than being a little more robust.
We also have non-list based methods like breaking the word down into smaller chunks to try to build up hopefully more novel rhymes. I imagine professionals have even more tools, given the complexity of more modern rhyme schemes.
It already knows which words are, statistically, more commonly rhymed with each other. From the massive list of training poems. This is what the massive data sets are for. One of the interesting things is that it’s not predicting backwards, exactly. It’s actually mathematically converging on the response text to the prompt, all the words at the same time.
Which is exactly how we do it. Ours is just a little more robust.
We also check to see if the word that popped into our heads actually rhymes by saying it out loud. Actual validation steps we can take is a bigger difference than being a little more robust.
We also have non-list based methods like breaking the word down into smaller chunks to try to build up hopefully more novel rhymes. I imagine professionals have even more tools, given the complexity of more modern rhyme schemes.