I "have tried (and continue to try) that human-in-the-loop data growth" to enormous success, bringing logistic regression models to greater than 99% accuracy. A

I "have tried (and continue to try) that human-in-the-loop data growth" to enormous success, bringing logistic regression models to greater than 99% accuracy. And you can chain vectorization strategies to create more input features than simply a bag-of-words, like morphology, shape, etc. We (the software company that I work for) don't need GPT-3, because it is a specialized model geared towards generating human-like text. Most NLP problems are just parsing text for actionable information, and oftentimes, supervised models can be chained to create something far more effective towards your needs than trying to shoehorn a massive general-purpose unsupervised model into a specialized problem.