A while back I wrote about language modeling without neural networks , where I generated Shakespeare with an unbounded n-gram model: no weights, no training, just counting. Fortuitously, I came across the paper Language Modeling is Compression , which mentioned the compression–prediction equivalence :

every prediction model is inherently a compressor, and all compression algorithms are prediction models .

This led to the natural question: can gzip do language modeling? 1 No neural network, no learned parameters, nothing. Just the compressor that ships with your operating system. You prime it with a corpus, give it a normal text prompt, and it continues that prompt by searching for the byte sequences that compress best.…