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About this paper
Pavel Chizhov, Catherine Arnett, Elizaveta Korotkova, Ivan P. Yamshchikov
A modified Byte-Pair Encoding algorithm, Picky BPE, refines tokenizer vocabularies during training by dynamically removing intermediate tokens using an Intersection over Self (IoS) metric. This approach maintains text compression while improving machine translation performance and reducing the presence of low L2 norm, potentially under-trained tokens in the final vocabulary.
Key insights
- In-training vocabulary refinement, guided by usage patterns, is more effective than post-processing for creating efficient and robust tokenizers.
- The IoS metric effectively identifies tokens that primarily serve as building blocks for longer tokens, enabling their removal without significant loss of information or compression.
- Maintaining a precise event order for merges and removals during both training and inference is critical for preserving text compression while refining vocabulary.
Summary by alphaXiv.
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