Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Advancements in Large Language Models (LLMs) have significantly enhanced instruction-following capabilities. However, most Instruction Fine-Tuning (IFT) datasets are predominantly in English, limiting model performance in other languages. Traditional methods for creating multilingual IFT datasets such as translating existing English IFT datasets or...
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Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
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TN_cdi_proquest_journals_3075443614
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3075443614
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2331-8422
