Summary of Instatrans: An Instruction-aware Translation Framework For Non-english Instruction Datasets, by Yungi Kim et al.
InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets
by Yungi Kim, Chanjun Park
First submitted to arxiv on: 2 Oct 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 The paper proposes a solution to generate high-quality instruction datasets for non-English languages by translating existing English datasets. The challenge lies in capturing tail phenomena, which limit performance on less frequent data. To address this, the authors introduce InstaTrans, a translation framework tailored for instruction datasets. Through experiments, they demonstrate the superiority of InstaTrans over competitors in terms of completeness and instruction-awareness of translation. This could broaden the accessibility of large language models (LLMs) across diverse languages at a relatively low cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make it easier to create good examples for teaching non-English languages. Right now, making these datasets is tricky because it’s hard to capture rare events that don’t happen often. To solve this problem, the authors suggest translating already-good English datasets into other languages. They created a special way to do this called InstaTrans, which does a great job of keeping the important information in the original dataset. By testing InstaTrans, they showed it works better than other methods and can make language models more accessible to people who don’t speak English. |
Keywords
» Artificial intelligence » Translation
