Summary of Toward a Theory Of Tokenization in Llms, by Nived Rajaraman and Jiantao Jiao and Kannan Ramchandran
Toward a Theory of Tokenization in LLMs
by Nived Rajaraman, Jiantao Jiao, Kannan Ramchandran
First submitted to arxiv on: 12 Apr 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 Transformers, a type of language model, are typically trained with tokenization, which involves breaking down text into individual tokens or words. Research has shown that tokenization is essential for designing high-performing language models. However, this paper takes a theoretical approach to investigate the behavior of transformers on simple data generating processes, specifically Markov chains. The study finds that when trained on data from these processes, transformers without tokenization fail to learn the correct distribution and predict characters according to a unigram model. With tokenization, however, transformers are able to model sequences drawn from the source near-optimally, achieving low cross-entropy loss. The paper also analyzes the end-to-end cross-entropy loss achieved by transformers with and without tokenization, showing that even simple unigram models can effectively model Markovian sources. This research provides a theoretical justification for using tokenization in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are language models that help us understand human language. They’re like super smart computers that can learn from lots of text data. But did you know that these models need something called tokenization to work really well? Tokenization is like breaking down words into tiny pieces, so the model can understand what each word means. This paper looked at how transformers behave when we don’t use tokenization and found that they struggle to learn correctly. When we do use tokenization, though, the models can understand sequences of text really well! The study also shows that even simple models can work well if we use tokenization. Overall, this research helps us understand why tokenization is important for language models. |
Keywords
» Artificial intelligence » Cross entropy » Language model » Tokenization
