Data Compression Explained (2012)

I think so, specifically lossy compression though.

A modern version of the book would include an extra section in the 'Lossy compression' chapter - 'Text' (alongside Images/Video/Audio) that would discuss LLM's.

It can and has been done just not very practical. Having a dozen GB language model just to squeeze out few more percent on plaintext compression which already compresses well and is tiny in comparison of images or video is not worth it outside benchmarks. Even superior traditional conpression algorithms are often not used due to insufficient software support. Multigabyte decompressor as big as rest of your OS installation is not practical to distribute or standardize. It would also take a lot of memory at runtime for decompressing thus shadowing the efficiency gains in everyday use. Only if you have huge archival scale of data it might be worth the gains. But for long term archival fragile formats which depend on huge arbitrary extra knowledge isnt a good idea. I am not quite sure if ai based compression would make it more robust by allowing to fix corruption based on context or make it worse by having single bitflip produce completely opposite but still plausible looking text. At least with traditional compression its usually obvious when corruption causes gibberish. And then you have problem of versioning, you need to have exactly the same version of dozen GB model for decompression as was used for compression. Just one of them is questionable now imagine having to store few dozen of them. Most computers have code for supporting at least half a dozen compression formats, and many of those are parametrized allowing single algorithm to handle multiple varations of the compression scheme, and then many apps bundle their own copies of compression library.