Can gzip be a language model?
summary
General-purpose compression algorithms like gzip can function as basic language models because they inherently assign lower bit-costs to data that mirrors patterns in their training corpus. By treating the compressed length of a prompt plus a candidate continuation as a probability score, one can generate text that mimics the style of a source text. This process effectively transforms a compression tool into a predictive engine without requiring neural networks or traditional training phases.
key points
- Compression and prediction are equivalent because both rely on identifying patterns; high-probability sequences require fewer bits to encode, meaning any compressor has an implicit probability model.
- Generating coherent text with gzip requires a beam search algorithm to look ahead at multiple bytes rather than picking the single next byte, which avoids quantization noise from integer byte lengths.
- To prevent the loop of repeating the same text verbatim, the scoring context must be limited to a recent tail of generated output rather than the entire history.
who should read it
Developers and data scientists interested in information theory, alternative language modeling techniques, and the basics of how compression algorithms like DEFLATE work.
discussion positive
consensus
Commenters generally agree that using compression algorithms to classify text by topic or language is a fast and effective, albeit simple, approach. There is a strong consensus that zstd is a superior general-purpose compression tool due to its balance of speed and ratio, though LZMA2 is preferred for maximum compression. The discussion also touches on the technical links between information theory, machine learning, and tokenization in LLMs.
top insight
The use of compression for classification is essentially a precursor to how tokenization works in Large Language Models, specifically through Byte Pair Encoding (BPE). BPE handles common prefixes and suffixes across different languages, allowing the model to produce encodings of varying lengths based on the language's structure. This reveals a deep connection between data compression and modern natural language processing.
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