Summary of Accelerating Multilingual Language Model For Excessively Tokenized Languages, by Jimin Hong and Gibbeum Lee and Jaewoong Cho
Accelerating Multilingual Language Model for Excessively Tokenized Languages
by Jimin Hong, Gibbeum Lee, Jaewoong Cho
First submitted to arxiv on: 19 Jan 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 This paper proposes a novel approach to accelerate text generation in languages other than English, which are often hampered by tokenizers that fragment texts into Unicode-level tokens. The authors introduce a framework that fine-tunes a pre-trained large language model (LLM) with a vocabulary set tailored to the target language, ensuring preserved performance while reducing token fragmentation. This targeted fine-tuning increases generation speed by a factor of 1.7, making it an efficient solution for multilingual text generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers better at understanding and writing in different languages. Right now, these computers have trouble breaking down words into small pieces when they’re not English. The authors came up with a new way to fix this problem by teaching the computer to understand the special rules of each language. This makes the computer much faster at generating text in those languages. It’s like having a super-smart translator that can write and understand many different languages! |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Text generation » Token
