Summary of Where Is the Signal in Tokenization Space?, by Renato Lui Geh and Honghua Zhang and Kareem Ahmed and Benjie Wang and Guy Van Den Broeck
Where is the signal in tokenization space?
by Renato Lui Geh, Honghua Zhang, Kareem Ahmed, Benjie Wang, Guy Van den Broeck
First submitted to arxiv on: 16 Aug 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 This paper investigates the limitations of traditional Large Language Models (LLMs) that rely on deterministic tokenizers to encode text. The study reveals that the assumption that the probability of a piece of text is equivalent to its canonical token sequence is not accurate. In fact, it is computationally hard to determine the most likely tokenization for an autoregressive LLM or calculate the marginal probability over all possible tokenizations. Surprisingly, the paper shows that aggregating the probabilities of non-canonical tokenizations can improve performance on various evaluation benchmarks, including those for transformers and state space models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how language models process text. Typically, these models use a specific way to break down words into smaller parts called tokens. But what if there are many different ways to do this? The study finds that it’s hard to figure out the best way to do it and even harder to calculate all the possible outcomes. However, by combining multiple tokenization methods, the researchers found that language models can perform better on certain tasks. |
Keywords
» Artificial intelligence » Autoregressive » Probability » Token » Tokenization
