[RamblingCTO]

Sorry, but not really. If you know what you do, you don't just pick an LLM. LLMs are trained/built for a specific task: text generation. Other models are trained on different tasks. If you know what you do, you compare models (I don't mean LLM models with that!) and choose the best performing. Just because LLMs receive more training doesn't mean they have a better performance. Very weird and flawed way of thinking. This is just hype thinking

[hitradostava]

I have to agree with the parent. LLMs are excellent at a large range of NLP tasks. Of course they are not going to replace all ML models, but when it comes to NLP they are clearly better than lots of trained models (e.g. https://arxiv.org/pdf/2310.18025).

[saberience]

LLMs are general purpose tools and absolutely are not better than trained models (using the latest techniques) for a specific task. I mean, that's obviously true if you think about it.

You can use similar datasets and the latest model architectures and if you train a model purely for sentiment analysis it will be better than frontier general purpose LLMs for sentiment analysis.

[RamblingCTO]

It's really mind-boggling that so many people disagree via downvotes that you compare models and choose the best performing one, independent of the hype ...