> It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.

https://arxiv.org/pdf/2309.10668

Transformers are also used in the top algorithm right now on the Large Text Compression Benchmark. https://bellard.org/nncp/nncp.pdf

> LLMs were not developed to be, do not function as, and are not use as data compression utilities.

Again, from a information theoretic view point, this is exactly what they are doing, how they where developed and how they function.

I don't know any serious researcher in ML that would find this claim even remotely controversial. It's really not just "a semantics game", its a part of a foundational understanding of the topic. If you want to understand LLMs from this perspective, a good place to start is with an auto-encoder which does try to learn a standard compression algorithm, the move on to more sophisticated embedding models (found in a lot of recommender systems) which try to learn an additional objective on top of minimizing reconstruction error. You'll then see that Transformers and all other major NN architectures fall out of these basic principles.

> Please, come knocking when a service provider exists that will use LLM's to compactly store your company data.

This is literally what every vectordb company does right now, as well as all "chat with your docs" type startups.