InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets

• URL: http://arxiv.org/abs/2410.01512v1
• Date: Wed, 2 Oct 2024 13:02:23 GMT
• Title: InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets
• Authors: Yungi Kim, Chanjun Park,
• Abstract summary: It is challenging to generate high-quality instruction datasets for non-English languages due to tail phenomena. We propose translating existing high-quality English instruction datasets as a solution. We introduce a new translation framework tailored for instruction datasets, named InstaTrans.
• Score: 2.530471185132544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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