Summary of Llamax: Scaling Linguistic Horizons Of Llm by Enhancing Translation Capabilities Beyond 100 Languages, By Yinquan Lu et al.
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
by Yinquan Lu, Wenhao Zhu, Lei Li, Yu Qiao, Fei Yuan
First submitted to arxiv on: 8 Jul 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 research paper presents a new approach to pre-training Large Language Models (LLMs) for multilingual translation tasks. The authors develop LLaMAX, a model that achieves significantly higher translation performance compared to existing open-source LLMs and performs on-par with specialized translation models on the Flores-101 benchmark. To achieve this, they conduct extensive multilingual continual pre-training on the LLaMA series models using various training strategies such as vocabulary expansion and data augmentation. The results show that LLaMAX can serve as a robust multilingual foundation model for low-resource languages. The authors also make their code and models publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language translation better. Currently, large language models are great at translating between popular languages like English and Spanish. But they’re not so good at translating between languages that don’t have much data or aren’t as well-known. To solve this problem, the researchers created a new way to train these models called LLaMAX. They tested it and found that it was better than other similar models at translating between many different languages. This is important because it means we can use these models to translate for people who speak less common languages. |
Keywords
» Artificial intelligence » Data augmentation » Llama » Translation
