LLMs as Lossy Compression: Understanding How They Learn

This research presents a novel perspective on LLMs, framing them as instances of lossy compression. The core argument is that LLMs learn by retaining only the information in their training data that is relevant to their objectives, effectively compressing the vast amount of data they are exposed to. The study demonstrates that the optimality of a model's compression is correlated with its downstream performance, suggesting a direct link between representational structure and actionable insights about model performance. This information-theoretic framing offers a unified approach to understanding how LLMs learn and generalize, potentially leading to improved training methodologies and model architectures. The research also highlights the importance of considering the data and training recipes used, as these factors can influence the compression characteristics of different LLMs. This work contributes to a deeper understanding of the inner workings of LLMs and provides a foundation for future research in this area.

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