3 ms·

It would be useful in conjunction with other data, but wouldn’t the model just learn the logic of the obfuscation code (unless perhaps you heavily regularised i

• luke-stanley 3y ago
  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