Exploring gzip as a Tool for Language Modeling

A recent exploration examined whether gzip could function as a language model without using neural networks or learned parameters.

The study referenced the compression-prediction equivalence, suggesting that all prediction models are also compression algorithms. By priming gzip with a text corpus and providing a prompt, the tool was able to generate continuations based on byte sequences that compress effectively.

Gzip operates using the DEFLATE algorithm, which compresses data by finding matches in a sliding window of recent text. This allows gzip to predict likely continuations based on previously seen text, scoring them by their compressed length.

The initial approach of selecting the best single byte for continuation proved ineffective due to quantization noise. Instead, a beam search method was implemented to evaluate multiple candidate continuations, significantly enhancing the quality of generated text.

The project utilized standard-library Python and zlib for implementation, with the code available for public use. The findings suggest that gzip has more potential for language modeling than previously recognized.