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Instruction tuning has shown great promise in improving the performance oflarge language models. However, research on multilingual instruction tuning hasbeen limited due to the scarcity of high-quality instruction-response datasetsacross different languages. To bridge this gap, we present Bactrian-X, acomprehensive multilingual parallel dataset of 3.4 million instruction-responsepairs across 52 languages. Leveraging this dataset, we train a set of adaptersusing low-rank adaptation (LoRA), which are lightweight components thatseamlessly integrate with large language models. These adapters have asubstantially lower parameter count than the base model, making them easilyreplaceable and usable as plug-ins for different languages or language groups.Extensive experiments in various multilingual evaluation settings demonstratethat models derived from LoRA-based training over Bactrian-X outperform boththe vanilla models and existing instruction-tuned models. The code and modelsare publicly available at https://github.com/mbzuai-nlp/bactrian-x

Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation