Language Modeling Is Compression
Authors: name: Grégoire Delétang, name: Anian Ruoss, name: Paul-Ambroise Duquenne, name: Elliot Catt, name: Tim Genewein, name: Christopher Mattern, name: Jordi Grau-Moya, name: Li Kevin Wenliang, name: Matthew Aitchison, name: Laurent Orseau, name: Marcus Hutter, name: Joel Veness

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.
Finally, we show that the prediction-compression equivalence allows us to use
any compressor (like gzip) to build a conditional generative model.