Computer Science > Computation and Language
[Submitted on 16 Sep 2023 (v1), last revised 31 Jan 2024 (this version, v2)]
Title:Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
View PDFAbstract: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.
Submission history
From: Pinzhen Chen [view email][v1] Sat, 16 Sep 2023 11:22:46 UTC (46 KB)
[v2] Wed, 31 Jan 2024 03:42:04 UTC (39 KB)
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