duh, this is not news. (general, fast and cheap) before decision models, you could pick only 2.
LLM as judges - generalized, but too slow. If you had to make millions of classifications a day, this will be the wrong approach. you won't/shouldn't use LLM to classify spam/no spam. hot dog/or something.
traditional classifiers, very specific 1 trick pony, super fast and cheap once built. If you need to make tons and tons of classifications, this would be the approach. but if you wanted a classifier right now for a novel problem, you need an expert to curate data, train and deploy.
decision models/jev - are generic, you can throw them at most generic classification problems, and they are good enough. it's a fine balance between general, fast and cheap. you get all 3
LLM as judges - generalized, but too slow. If you had to make millions of classifications a day, this will be the wrong approach. you won't/shouldn't use LLM to classify spam/no spam. hot dog/or something.
traditional classifiers, very specific 1 trick pony, super fast and cheap once built. If you need to make tons and tons of classifications, this would be the approach. but if you wanted a classifier right now for a novel problem, you need an expert to curate data, train and deploy.
decision models/jev - are generic, you can throw them at most generic classification problems, and they are good enough. it's a fine balance between general, fast and cheap. you get all 3