GPT-3 can run code
> GPT-3 struggles with large numbers, decimal numbers, and negative numbers. When used it returns answers that are close but often incorrect.Regarding GPT-3's "guesstimates," intuitively it feels like the network has to guess because it hasn't been given a way to do exact computation--a neural network is built out of nonlinear functions--even if it "understands" the prompt (for whatever value you want to give to "understand").Are there any techniques that involve giving the model access to an oracle and allowing it to control it?  To continue the analogy, this would be the equivalent of giving GPT-3 a desk calculator.If this is a thing, I have other questions.  How do you train against it?  Would the oracle have to be differentiable?  (There are multiple ways to operate a desk calculator to evaluate the same expression.)  Also, what control interface would the model need so that it can learn to use the oracle?  (Would GPT-3 emit a sequence of 1-hot vectors that represent functions to do, and would the calculator have "registers" that can be fed directly from the input text?  Some way of indirectly referring to operands so the model doesn't have to lossily handle them.)