Probably disappointing to the authors but an excellent rebuttal.
This is the sort of mistake that's awfully easy to make in ML. The unfortunate thing about the field is that subtle methodological errors often cause subtle failures rather than catastrophic failures as we're used to in many other branches of engineering or science. You can easily make a system slightly worse or slightly better by contaminating your training set with bad data or accidentally leaking some information about your target and the ML system will take it in stride (but with slightly contaminated results).
This result makes sense to me because as much as I would like it to be true, applying existing compression algorithms to ML feels like too much of a "free lunch". If there was any special magic happening in compression algorithms we'd use compression algorithms as encoders instead of using transformers as compressors.
This is the sort of mistake that's awfully easy to make in ML. The unfortunate thing about the field is that subtle methodological errors often cause subtle failures rather than catastrophic failures as we're used to in many other branches of engineering or science. You can easily make a system slightly worse or slightly better by contaminating your training set with bad data or accidentally leaking some information about your target and the ML system will take it in stride (but with slightly contaminated results).
This result makes sense to me because as much as I would like it to be true, applying existing compression algorithms to ML feels like too much of a "free lunch". If there was any special magic happening in compression algorithms we'd use compression algorithms as encoders instead of using transformers as compressors.