Summary of Tokenization Counts: the Impact Of Tokenization on Arithmetic in Frontier Llms, by Aaditya K. Singh et al.
Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs
by Aaditya K. Singh, DJ Strouse
First submitted to arxiv on: 22 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 The proposed research investigates the impact of tokenization schemes on the performance of large language models (LLMs) in numerical reasoning tasks. Specifically, it examines how different tokenization methods for numbers (e.g., single-digit, separate tokens for each digit) influence arithmetic task outcomes. The study finds that right-to-left tokenization leads to improved performance, while standard left-to-right tokenization exhibits stereotyped error patterns suggesting systematic computations. Furthermore, the research demonstrates that models can convert between tokenizations and recover performance on left-to-right tokenized inputs using chain-of-thought-inspired approaches. As LLMs scale, the gap between tokenization directions decreases, possibly indicating better inductive bias override. This study highlights the importance of considering number tokenization choices when developing general models of numerical reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models (like those used for smart chatbots) do math problems. The researchers looked at different ways of splitting up numbers into “tokens” (think of it like breaking down a phone number into individual digits). They found that one way, called “right-to-left,” makes the model better at math. Another way, called “left-to-right,” makes the model make mistakes in certain patterns. The researchers also showed that bigger models can overcome these differences and do well on both types of tokenization. This study is important because it helps us understand how to make language models better at doing math. |
Keywords
* Artificial intelligence * Tokenization
