| In the sense of GPT-3. I guess you can ask GPT-3 to classify things and it gets the right answer... Sometimes. You might have some domain where the new models work for you and I'd love to hear you talk about it. I see a lot of papers go by in arXiv and also blog postings by data science people and would say that the behavior of a classifier can be limited by many things. For the most part bag-of-word classifiers do very well for classifying topics because topics involve very different vocabulary. They do not do so well at sentiment analysis where you have to know "not good" = "bad". I worked at one place that had a CNN classifier that could classify random snippets as "address", "full name", etc. but it wasn't able to learn how to calculate credit card checksums. For some problems 90% accuracy is very bad (e.g. predict some event that happens 1 in 10 times like a headline getting a few comments on HN -- a fancy classifier could probably do better than my simple classifier it is not going put up a dramatically better AUC because of the fuzziness of the problem) Even crisper concepts get controversial around around 1 time in 20. My simple classifier is fast to learn I like articles about classifiers and don't like articles about theoretical CS but it struggles to tell I like the NFL and hate the Premier League, a fancy classifier could do better, and I will give one a chance because i have the data to do it with. With the simple classifier it is simple to do cross-validation, parametric tuning and such, but usually people publishing results on deep models do not publish error bars, do not understand how the quality of the model varies from run-to-run, etc. Even if you ask ChatGPT to do it you will need to supply a large number of test cases to prove it gets the result. |