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The Relationship Between Data Compression and Large Language Models
Key points
- Compressors and LLMs solve the same problem: prediction.
- True compression relies on identifying and exploiting data redundancy.
- Minification is not considered true compression as it removes unnecessary syntax.
- Modern compression tools use transforms, models, and entropy coders.
Compression and LLMs: A Shared Goal
Data compression and large language models (LLMs) fundamentally address the same problem: prediction. Both systems aim to identify patterns and redundancy within data to represent it more efficiently or generate coherent sequences. This shared objective highlights a deep, often overlooked, connection between these two seemingly disparate fields.
Understanding True Compression
While methods like minification reduce file size by stripping unnecessary syntax (e.g., whitespace, comments, human-readable variable names), they are not considered 'true' compression. True compression relies on identifying and exploiting redundancy within data to encode it into a shorter form. For example, a string of repeating characters can be represented more compactly by noting the character and its repetition count, as seen in run-length encoding.
Mechanisms of Modern Compression
Modern compression tools, such as gzip and Brotli, employ a combination of techniques to achieve significant data reduction. These typically include 'transforms,' which are preprocessing steps that make data more amenable to compression, sometimes by increasing redundancy. Other components often involve 'models' for statistical analysis and 'entropy coders' for efficient encoding of the processed data.
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Primary sources
arXiv 2309.10668Reporting from
This article explains the fundamental connection between data compression techniques and the underlying principles of large language models (LLMs). It details how both fields essentially address the same problem of prediction, using redundancy to condense information. Understanding this relationship provides insight into how LLMs process and generate language.