Yes in conjunction. For causal models like GPT, this sort of approach might help augment data, making it go further when training, or fine-tuning and the model

Yes in conjunction. For causal models like GPT, this sort of approach might help augment data, making it go further when training, or fine-tuning and the model could be trained on "improving" the bad code, actually the existing code. So the before and after would be reversed. For encoder-decoder models they can can be trained, or fine-tuned on a more explicit translation task. So just a kind of https://en.wikipedia.org/wiki/Data_augmentation https://en.wikipedia.org/wiki/Data_augmentation