Can gzip be a language model?

In a thought-provoking exploration, Nathan R. examines the unconventional intersection of data compression and machine learning. The article investigates whether the gzip algorithm, traditionally used for file compression, can function as a rudimentary language model. By leveraging the Normalized Compression Distance (NCD) metric, the author demonstrates that simple compression-based techniques can perform surprisingly well on text classification tasks, often rivaling more complex deep learning models in specific low-resource scenarios. The piece delves into the theoretical underpinnings of how compression captures patterns and statistical redundancies in data, effectively acting as a proxy for understanding language structure. While acknowledging that gzip lacks the generative capabilities and contextual depth of modern Large Language Models, the author highlights the efficiency and simplicity of this approach. This experiment serves as a compelling reminder that foundational information theory principles remain highly relevant in the era of advanced artificial intelligence.