Summary of Spacebyte: Towards Deleting Tokenization From Large Language Modeling, by Kevin Slagle
SpaceByte: Towards Deleting Tokenization from Large Language Modeling
by Kevin Slagle
First submitted to arxiv on: 22 Apr 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 In this paper, researchers address the limitations of tokenization in large language models by proposing a novel byte-level decoder architecture called SpaceByte. Tokenization has been shown to improve model performance, but it also introduces biases, makes models more vulnerable to adversarial attacks, and decreases their ability to model character-level relationships. To overcome these disadvantages without sacrificing performance, the authors design SpaceByte as a combination of a byte-level Transformer model with larger transformer blocks inserted in the middle of the layers. They experimentally show that by applying these larger blocks only after certain bytes, such as space characters denoting word boundaries, they can achieve significant improvements in performance. SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures for a fixed training and inference compute budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SpaceByte is a new way to build language models that improves their performance without using tokens. Tokens are like little boxes that break up words into smaller parts, but they can also make the model biased or vulnerable to attacks. The authors of this paper created SpaceByte by combining two types of Transformer models. They inserted special blocks in the middle of the layers to help the model understand word boundaries better. This made their model perform just as well as other more complex models that use tokens, but with less computational power needed. |
Keywords
» Artificial intelligence » Decoder » Inference » Tokenization » Transformer
