[yacine_]

If you fine tune them to be task specific, they'll perform well. In my experience, this control loop is a better investment than "prompt engineering". (When I say task specific, I mean very task specific)

GPT4 over the API is too fine tuned, which constrains its behavior. It fails to capture nuance in instructions. When you have the bag of weights, you can actually control your model. Having actual control over the model, and understanding the infrastructure that it's running on helps you meet actual SLAs.

And it's cheaper, if you're not backed by infinite venture money.

https://arxiv.org/abs/2307.13269

[MuffinFlavored]

Could you explain what you mean by fine-tune? For example, I don't have the answers to what the songs parsed out + genre identified into JSON looks like. You're saying I'd have to train the model with known answers, and then maybe it could predict with some accuracy going forward?

I don't see how this warrants the extra exciting popularity of LLAMA2, etc.

I still haven't found my own personal niche "good enough" test case

[yacine_]

Here's an example: https://paperswithcode.com/task/coreference-resolution