If I read this correctly, the largest test reported on this page is the "enwik9" dataset, which compresses to 213 MB with xz and only 135 MB with this method, a 78 MB difference... using a model that is 340 MB (and was probably trained on the test data).
No one would be impressed with saving 78 MB on compression using a 340 MB dictionary so I am not sure why this is good?
No one would be impressed with saving 78 MB on compression using a 340 MB dictionary so I am not sure why this is good?
Please let me know if I misunderstand.