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. Here’s some real, unedited output after priming it on tiny Shakespeare: gzipt --corpus data/tinyshakespeare.txt --prompt $'MENENIUS:\n' --length 200 MENENIUS: 'Though all at once canq MARCIUS: Pray now, nocamest thou to a morsel .…