Summary of Multi-word Tokenization For Sequence Compression, by Leonidas Gee et al.
Multi-word Tokenization for Sequence Compression
by Leonidas Gee, Leonardo Rigutini, Marco Ernandes, Andrea Zugarini
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 MWT, a novel Multi-Word Tokenizer, revolutionizes language processing by representing frequent multi-word expressions as single tokens, boosting performance and efficiency. By compacting tokenization, MWTs enable faster inference and increased coverage of input data, even at shorter sequence lengths. This breakthrough yields two key benefits: improved performance with reduced computational costs and speedups via early truncation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MWT is a new way to understand language. It groups common phrases together into single units, making it easier for computers to process text. This helps machines learn faster and make better predictions. MWT also lets us stop processing text earlier without losing accuracy, which makes it much quicker. |
Keywords
* Artificial intelligence * Boosting * Inference * Tokenization * Tokenizer
