Summary of Counting Ability Of Large Language Models and Impact Of Tokenization, by Xiang Zhang et al.
Counting Ability of Large Language Models and Impact of Tokenization
by Xiang Zhang, Juntai Cao, Chenyu You
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A recent paper investigates the limitations of Transformers in large language models (LLMs), particularly their inability to perform increasingly deep reasoning as input length grows. Unlike recurrent networks, Transformers are theoretically incapable of solving tasks that require growing depth due to their constant-depth computation architecture. The authors examine how tokenization affects counting abilities in LLMs and provide both theoretical and experimental analyses. They find that different tokenization choices can substantially impact performance, with implications for the design of new tokenization methods. This study highlights the importance of considering tokenization when designing LLMs to enhance their reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers understand language. It talks about a problem with a popular tool called Transformers that makes it hard for them to get better at understanding longer texts. The researchers found that the way we break up text into smaller pieces, or tokenization, affects how well these models do their job. They studied different ways of breaking up text and showed that some are much better than others. This study helps us understand how to make language models better at understanding long texts. |
Keywords
» Artificial intelligence » Tokenization
