Summary of Mrt5: Dynamic Token Merging For Efficient Byte-level Language Models, by Julie Kallini et al.
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
by Julie Kallini, Shikhar Murty, Christopher D. Manning, Christopher Potts, Róbert Csordás
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces MrT5, an efficient variant of the ByT5 model that integrates token deletion to shorten input sequences. MrT5 uses a learned delete gate to dynamically remove tokens and retain critical information in subsequent layers. This approach enables faster inference with minimal performance loss, achieving significant gains in bits-per-byte on pre-training experiments. The model also adapts to language-specific compression rates during multilingual training. Comparably accurate to ByT5, MrT5 reduces sequence lengths by up to 75% on downstream evaluations like XNLI and TyDi QA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new model called MrT5 that helps make computers understand text more efficiently. The current models are good but can get stuck when they see errors in spelling or different writing styles. This makes them slower and harder to use. MrT5 is like a special filter that looks at the important words and keeps them, while getting rid of less important ones. This makes it faster and still works just as well. |
Keywords
* Artificial intelligence * Inference * Token
