Summary of Llamaturk: Adapting Open-source Generative Large Language Models For Low-resource Language, by Cagri Toraman
LlamaTurk: Adapting Open-Source Generative Large Language Models for Low-Resource Language
by Cagri Toraman
First submitted to arxiv on: 13 May 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 study aims to enhance global accessibility by adapting large language models trained on English to low-resource languages. The primary methods for representing these languages are monolingual and multilingual pretraining. However, these methods have limitations, including high hardware requirements and uneven performance across languages. To address this issue, the authors explore various strategies for adapting large language models to low-resource languages, including continual training, instruction fine-tuning, task-specific fine-tuning, and vocabulary extension. The results show that continual training improves language comprehension, as reflected in perplexity scores, and task-specific tuning generally enhances performance of downstream tasks. However, extending the vocabulary shows no substantial benefits. Additionally, while larger models improve task performance with few-shot tuning, multilingual models perform worse than their monolingual counterparts when adapted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to make language models more accessible globally by adapting them to low-resource languages. The authors try different ways to do this, including training the model a little bit longer, fine-tuning it for specific tasks, and adding new words to the vocabulary. They find that some of these methods work better than others, but that bigger models are not always better. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Perplexity » Pretraining
