There used to be an entire sub-field of NLP called Open Domain Question Answering (ODQA). It extensively studied the problem of selecting the best answer from the set of plausible answers and devised a number of potential strategies. Like everything else in AI/ML it fell victim to the "bitter lesson", in this case that scaling up "predict the next token" beats an ensemble of specialized linguistic-based methods.
I agree with you for the NLP domain, but I wonder if there will also be a bitter lesson learned about the perceived generality of language for universal applications.
