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What's the consensus of building state of the art classifiers? Is using Llama2, Mistral, and co. really better than BERT? Fine tuning vs prompt engineering (whi
by tannhaeuser 3y ago
What's the consensus of building state of the art classifiers? Is using Llama2, Mistral, and co. really better than BERT? Fine tuning vs prompt engineering (which is what I understand the article to be about)?Text classification can also range from mere sentiment analysis, to genre/form classification, to content classification, and there are conflicting results about the relative merits of LLMs for genre vs content classification at least, ie. arguing that pretraining on large data sets can't effectively be undone and specialized using just ~1000 texts on a given knowledge vertical, as agency and GPU cloud hosters want you make to believe.
- bugglebeetle 3y agoGPT-4 with JSON function calling is about on par with BERT and company for simple classification problems, but is slower and more expensive. For anything more complicated than that, you’re better off using standard ML models. One way they can be used to build on each other, however, is synthetic training data generation from LLMs to train other kinds of models. In my experience, this works best when you already have a fairly robust dataset for it to create permutations from.