  Tokenization is a foundational step in Natural Language Processing (NLP)
tasks, bridging raw text and language models. Existing tokenization approaches
like Byte-Pair Encoding (BPE) originate from the field of data compression, and
it has been suggested that the effectiveness of BPE stems from its ability to
condense text into a relatively small number of tokens. We test the hypothesis
that fewer tokens lead to better downstream performance by introducing
PathPiece, a new tokenizer that segments a document's text into the minimum
number of tokens for a given vocabulary. Through extensive experimentation we
find this hypothesis not to be the case, casting doubt on the understanding of
the reasons for effective tokenization. To examine which other factors play a
role, we evaluate design decisions across all three phases of tokenization:
pre-tokenization, vocabulary construction, and segmentation, offering new
insights into the design of effective tokenizers. Specifically, we illustrate
the importance of pre-tokenization and the benefits of using BPE to initialize
vocabulary construction. We train 64 language models with varying tokenization,
ranging in size from 350M to 2.4B parameters, all of which are made publicly
available.
