Summary of Language Very Rare For All, by Ibrahim Merad et al.
Language verY Rare for All
by Ibrahim Merad, Amos Wolf, Ziad Mazzawi, Yannick Léo
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 In the field of machine translation, encoder-decoder models like NLLB have made significant progress in translating rare languages, with some models even trainable on a single GPU. While general-purpose large language models (LLMs) perform well in translation tasks, open LLMs fine-tuned for specific tasks involving unknown corpora prove highly competitive. This study introduces LYRA, a novel approach combining open LLM fine-tuning, retrieval-augmented generation, and transfer learning from high-resource languages to facilitate rare language translation. The proposed method is exclusively focused on single-GPU training to promote ease of adoption. Our results demonstrate LYRA’s effectiveness in two-way translation between French and Monégasque, a rare language unsupported by existing translation tools due to limited corpus availability. LYRA frequently surpasses and consistently matches state-of-the-art encoder-decoder models in rare language translation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to communicate with someone who speaks a very rare language that you’ve never heard before. This is a big problem for people who want to understand each other, but it’s especially hard when there’s not enough data available. Researchers have been working on finding ways to overcome this challenge. They’ve developed new models called NLLB and LYRA that can help translate languages we don’t know very well. The goal of these models is to make communication easier by allowing us to understand each other, even if we’re speaking different languages. |
Keywords
» Artificial intelligence » Encoder decoder » Fine tuning » Retrieval augmented generation » Transfer learning » Translation
