InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912055698653184 |
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| author | Kim, Yungi Park, Chanjun |
| author_facet | Kim, Yungi Park, Chanjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01512 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets Kim, Yungi Park, Chanjun Computation and Language Artificial Intelligence 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. |
| title | InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.01512 |