Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca

• URL: http://arxiv.org/abs/2309.08958v2
• Date: Wed, 31 Jan 2024 03:42:04 GMT
• Title: Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
• Abstract summary: This study employs the Alpaca dataset and machine translations of it to form multilingual data. Under a controlled budget, comparisons show that multilingual tuning is on par or better than tuning a model for each language. Our findings serve as a guide for expanding language support through instruction tuning.
• Score: 32.66997811671922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. While such efforts are often carried out in a single language, we empirically analyze cost-efficient strategies for multilingual scenarios. Our study employs the Alpaca dataset and machine translations of it to form multilingual data, which is then used to tune LLMs through either low-rank adaptation or full-parameter training. Under a controlled computation budget, comparisons show that multilingual tuning is on par or better than tuning a model for each language. Furthermore, multilingual tuning with downsampled data can be as powerful and more robust. Our findings serve as a guide for expanding language support through instruction tuning.

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