A data scientist trains two language models, Model Alpha and Model Beta, on the same large corpus of text. After training, Model Alpha consistently achieves a lower cross-entropy loss on unseen test data compared to Model Beta. Considering the principle that better prediction is equivalent to better compression, what is the most accurate interpretation of this result?

The Link Between Prediction and Compression

Evaluating LLM Specialization through the Lens of Compression