How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM

• URL: http://arxiv.org/abs/2404.04850v2
• Date: Mon, 09 Dec 2024 18:25:51 GMT
• Title: How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM
• Authors: Shaoxiong Ji, Pinzhen Chen,
• Abstract summary: It is yet to be determined whether having a handful of languages is sufficient, or whether the benefits increase with the inclusion of more. By fine-tuning large multilingual models on 1 to 52 languages, we present a case study on BLOOM to understand three pertinent factors affecting performance. Overall we found that 1) expanding language coverage in multilingual instruction tuning proves to be beneficial; 2) accuracy often significantly boots if the test language appears in the instruction mixture; and 3) languages' genetic features correlate with cross-lingual transfer more than merely the number of language but different languages benefit to various degrees.
• Score: 4.56830243967663
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• Abstract: Instruction tuning a large language model with multiple languages can prepare it for multilingual downstream tasks. Nonetheless, it is yet to be determined whether having a handful of languages is sufficient, or whether the benefits increase with the inclusion of more. By fine-tuning large multilingual models on 1 to 52 languages, we present a case study on BLOOM to understand three pertinent factors affecting performance: the number of languages, language exposure, and similarity between training and test languages. Overall we found that 1) expanding language coverage in multilingual instruction tuning proves to be beneficial; 2) accuracy often significantly boots if the test language appears in the instruction mixture; 3) languages' genetic features correlate with cross-lingual transfer more than merely the number of language but different languages benefit to various degrees.

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