Unpacking Tokenization: Evaluating Text Compression and its Correlation
  with Model Performance
Omer Goldman Avi Caciularu Matan Eyal Kris Cao Idan Szpektor Reut Tsarfaty
Despite it being the cornerstone of BPE, the most common tokenization
algorithm, the importance of compression in the tokenization process is still
unclear. In this paper, we argue for the theoretical importance of compression,
that can be viewed as 0-gram language modeling where equal probability is
assigned to all tokens. We also demonstrate the empirical importance of
compression for downstream success of pre-trained language models. We control
the compression ability of several BPE tokenizers by varying the amount of
documents available during their training: from 1 million documents to a
character-based tokenizer equivalent to no training data at all. We then
pre-train English language models based on those tokenizers and fine-tune them
over several tasks. We show that there is a correlation between tokenizers'
compression and models' downstream performance, suggesting that compression is
a reliable intrinsic indicator of tokenization quality. These correlations are
more pronounced for generation tasks (over classification) or for smaller
models (over large ones). We replicated a representative part of our
experiments on Turkish and found similar results, confirming that our results
hold for languages with typological characteristics dissimilar to English. We
conclude that building better compressing tokenizers is a fruitful avenue for
further research and for improving overall model performance.