I don’t know why, but the fact that the model was good at it makes explicit training not implausible, the most likely source is that if you just place in text it is very often labeled by author, and they might have scrubbed that for data quality reasons, because they don’t actually care, and stylometry came from a transfer from things they did for other reasons and stopped.
I was saying mainly that even though explicit training was not the likely prior case, we can only tell that they probably reduced training direction towards stylometry through suppression , removing post training that helped, or removing pretraining structures that helped, and not their previous position on that axis. It might have been the case that they were limiting stylometry before a little, and are now doing so a lot or a lot more effectively.
For instance, if they moved to more synthetic data stylometry might have gotten hit as a side effect because the human corpus shrunk, and so precision and recall went down enough that it got hit by honesty training.
This probably breaks if you separate tags by injecting a designed vector as reference, or even move from projecting a one hot matrix of token identity to predicting a two hot matrix of tok+role, but that is not something that most labs do, so that might be a mitigation, have something automated provide good flagging instead, but it involves injection so it has other issues.
Oh wait, this is a repeat comment.