用指令感知翻译框架提升非英语指令数据质量
InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets
- 设计指令感知翻译框架InstaTrans，确保翻译忠实原意
- 在多个语言上验证，翻译后微调模型性能显著提升
- 适合想低成本拓展大模型多语言能力的研究者
由于长尾现象，生成高质量的非英语指令数据集极具挑战性。为此，我们提出通过翻译高质量英语指令数据集来解决该问题，强调翻译需完整且保持指令语义一致性。我们提出专为指令数据集设计的翻译框架InstaTrans（INSTruction-Aware TRANSlation）。实验表明，InstaTrans在翻译完整性与指令感知性方面优于现有方法，具备低成本扩大大模型多语言覆盖的潜力。进一步验证显示，使用InstaTrans翻译的数据微调大模型，可有效提升其在目标语言上的表现。
原文摘要 · Abstract (English)
It is challenging to generate high-quality instruction datasets for non-English languages due to tail phenomena, which limit performance on less frequently observed data. To mitigate this issue, we propose translating existing high-quality English instruction datasets as a solution, emphasizing the need for complete and instruction-aware translations to maintain the inherent attributes of these datasets. We claim that fine-tuning LLMs with datasets translated in this way can improve their performance in the target language. To this end, we introduces a new translation framework tailored for instruction datasets, named InstaTrans (INSTruction-Aware TRANSlation). Through extensive experiments, we demonstrate the superiority of InstaTrans over other competitors in terms of completeness and instruction-awareness of translation, highlighting its potential to broaden the accessibility of LLMs across diverse languages at a relatively low cost. Furthermore, we have validated that fine-tuning LLMs with datasets translated by InstaTrans can effectively improve their performance in the target language.
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