I don't agree it's cheap. For generation at fairly small scale, sure, but generation is just the party trick. The real power for my use case lies in how much better it seems to do at traditional NLP tasks than an out-of-the-box model, with no further fiddling and faffing required.
Say I've got a corpus of ~1m documents, each of 10+ paragraphs and I want to run quote extraction on them (it does this beautifully), vectorise them for similarity search, whatever. This gets pretty expensive pretty fast.
Say I've got a corpus of ~1m documents, each of 10+ paragraphs and I want to run quote extraction on them (it does this beautifully), vectorise them for similarity search, whatever. This gets pretty expensive pretty fast.