Summary of Tokenization Is More Than Compression, by Craig W. Schmidt et al.
Tokenization Is More Than Compression
by Craig W. Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine, Omri Uzan, Yuval Pinter, Chris Tanner
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the role of tokenization in natural language processing tasks, challenging the idea that fewer tokens lead to better downstream performance. The authors introduce PathPiece, a new tokenizer that segments text into the minimum number of tokens for a given vocabulary. Through extensive experimentation, they find that this hypothesis is not supported, casting doubt on the understanding of effective tokenization. Instead, the study highlights the importance of pre-tokenization and the benefits of using Byte-Pair Encoding (BPE) to initialize vocabulary construction. The authors train 64 language models with varying tokenization, ranging in size from 350M to 2.4B parameters, and make them publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we split text into smaller parts called tokens. Right now, some tools use a method called Byte-Pair Encoding (BPE) that comes from data compression. People thought that using fewer tokens was better for language models, but this study says it’s not true. Instead, the authors create a new way to split text called PathPiece and test different ways of doing tokenization. They find that how we prepare the text before splitting it into tokens is important, and that BPE can help with building a good vocabulary. The researchers also train many language models using different tokenization methods and make them available for others to use. |
Keywords
» Artificial intelligence » Natural language processing » Tokenization » Tokenizer
