Summary of Exploring Design Choices For Building Language-specific Llms, by Atula Tejaswi et al.
Exploring Design Choices for Building Language-Specific LLMs
by Atula Tejaswi, Nilesh Gupta, Eunsol Choi
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper investigates the development of language-specific large language models (LLMs) by adapting monolingual and multilingual LLMs. The authors conduct experiments to explore how design choices, such as base model selection, vocabulary extension, and continued pretraining, impact the adapted LLM’s efficiency and end-task performance. They find that initial performance does not always correlate with final performance after adaptation, and that adapting English-centric models can yield better results than multilingual models. Additionally, they discover that efficiency can be improved through simple vocabulary extension and continued pretraining in most LLMs studied. Furthermore, the optimal adaptation method is highly language-dependent, and the simplest embedding initialization works well across various experimental settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at creating special large language models for specific languages by changing existing models. The researchers test different ways to adapt these models, like choosing a good base model or adding new words, to see how it affects their performance. They find that sometimes the starting point of the model doesn’t matter as much as the final result after making changes. They also discover that using English-based models can be better than using multilingual models for some languages, and that making small tweaks to the model can make it more efficient. |
Keywords
» Artificial intelligence » Embedding » Pretraining
