That's a fairly sane request, but I think people can do a bit better yet. This is a bug, and IMO the correct solution is to fix it. Preferably people should n

That's a fairly sane request, but I think people can do a bit better yet. This is a bug, and IMO the correct solution is to fix it. Preferably people should nip it in the bud at the start of the process, rather than play whack-a-mole with the outcome. When your model gives you back exactly the same data as was put in, that's called "overfitting". It's a kind of bug (or at very least a smell). It means (among other things) that it might actually give back the same answer in other situations where it is wrong to do so. It could then also give a different, wrong answer in a situation where this answer was actually the correct one. It also means that the model spent all this time "learning the answer by heart" instead of the underlying pattern. This is typically a waste of perfectly good training time if nothing else. And of course if you have an overfit, in this case you end up suggesting copyrighted code to the user instead of something original. This is rather undesirable. In one of the cases I looked at with a friend, we think many people had actually copied one person's code without attribution. So the training set might contain -say- 100 copies of that same solution. That would then be a strong signal that there is only one correct/common solution to that particular problem: "Copy this one guy and leave off the attribution". Oops. I wonder how many complaints about copilot are actually +/- "blaming the messenger". I suspect that fixing/improving/mitigating this particular set of bugs may have an unforseen side effect: a number of rather interesting conversations with some of the developers in the source data-set.