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arXiv · 2309.08958

Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca

Pinzhen Chen·Shaoxiong Ji·Nikolay Bogoychev·Andrey Kutuzov·Barry Haddow·Kenneth Heafield
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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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Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev, Andrey Kutuzov, Barry Haddow, Kenneth Heafield. 2023-09-16. Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca. https://arxiv.org/abs/2309.08958

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