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There is an excellent talk by Jack Rae called “compression for AGI”, where he shows (what I believe to be) a little known connection between transformers and compression;

In one view, you can view LLMs as SOTA lossless compression algorithms, where the number of weights don’t count towards the description length. Sounds crazy but it’s true.



his talk here https://www.youtube.com/watch?v=dO4TPJkeaaU

and his last before departing for Meta Superintelligence https://www.youtube.com/live/U-fMsbY-kHY?si=_giVEZEF2NH3lgxI...


A transformer that doesn't hallucinate (or knows what is a hallucination) would be the ultimate compression algorithm. But right now that isn't a solved problem, and it leaves the output of LLMs too untrustworthy to use over what are colloquially known as compression algorithms.


It is still task related.

Compressing a comprehensive command line reference via model might introduce errors and drop some options.

But for many people, especially new users, referencing commands, and getting examples, via a model would delivers many times the value.

Lossy vs. lossless are fundamentally different, but so are use cases.




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