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[-] Harlehatschi@lemmy.ml 6 points 1 year ago* (last edited 1 year ago)

Ok so the article is very vague about what's actually done. But as I understand it the "understood content" is transmitted and the original data reconstructed from that.

If that's the case I'm highly skeptical about the "losslessness" or that the output is exactly the input.

But there are more things to consider like de-/compression speed and compatibility. I would guess it's pretty hard to reconstruct data with a different LLM or even a newer version of the same one, so you have to make sure you decompress your data some years later with a compatible LLM.

And when it comes to speed I doubt it's nearly as fast as using zlib (which is neither the fastest nor the best compressing...).

And all that for a high risk of bricked data.

[-] barsoap@lemm.ee 4 points 1 year ago

I would guess it’s pretty hard to reconstruct data with a different LLM

I think the idea is to have compressor and decompressor use the exact same neural network. Looks like arithmetic coding with a learned function.

But yes model size is probably going to be an issue.

[-] Harlehatschi@lemmy.ml 2 points 1 year ago

Ye but that would limit the use cases to very few. Most of the time you compress data to either transfer it to a different system or to store it for some time, in both cases you wouldn't want to be limited to the exact same LLM. Which leaves us with almost no use case.

I mean... cool research... kinda.... but pretty useless.

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this post was submitted on 15 May 2025
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