The author explores the compression–prediction equivalence from information theory by asking whether gzip can function as a language model. By priming gzip with a Shakespeare corpus and using it to continue prompts, they demonstrate that compression algorithms inherently encode predictive structure, producing partially coherent but notably garbled text output.
Background
The compression-prediction equivalence, rooted in Shannon's information theory, states that optimal compression and optimal prediction are mathematically dual problems. Recent interest in non-neural language modeling has renewed attention to classical algorithmic approaches.
- Source
- hackernews
- Published
- Sep 22, 2026 at 02:08 PM
- Score
- 6.0 / 10