Summary of Transcending Language Boundaries: Harnessing Llms For Low-resource Language Translation, by Peng Shu et al.
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
by Peng Shu, Junhao Chen, Zhengliang Liu, Hui Wang, Zihao Wu, Tianyang Zhong, Yiwei Li, Huaqin Zhao, Hanqi Jiang, Yi Pan, Yifan Zhou, Constance Owl, Xiaoming Zhai, Ninghao Liu, Claudio Saunt, Tianming Liu
First submitted to arxiv on: 18 Nov 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 tackles the underexplored realm of Large Language Models (LLMs) in low-resource language translation. Specifically, it examines the challenges of translating into minority languages like Cherokee, Tibetan, and Manchu, which are critical for cultural preservation and development. To address this issue, the authors introduce a novel retrieval-based method that enhances translation quality by focusing on key terms. This approach translates keywords and retrieves corresponding examples from existing data. The paper evaluates the effectiveness of this method through experiments translating from English into these low-resource languages, comparing it to the performance of GPT-4o and LLaMA 3.1 405B. The results highlight the significant challenges faced by these models in low-resource language translation, while demonstrating the promise of the retrieval-based method in improving word-level accuracy and overall semantic understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping machines understand languages that are not well-known or widely spoken. These languages are important for keeping cultures alive and allowing people to communicate with each other. The authors found that current language models struggle to translate these languages accurately, which makes it hard for minority communities to share their culture and ideas. To solve this problem, they developed a new way of translating using existing data. They tested this method on three languages: Cherokee, Tibetan, and Manchu. Their results show that this method can help improve translation accuracy and understanding. |
Keywords
» Artificial intelligence » Gpt » Llama » Translation
