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Instruction tuning is essential for large language models (LLMs) to becomeinteractive. While many instruction tuning datasets exist in English, there isa noticeable lack in other languages. Also, their effectiveness has not beenwell verified in non-English languages. We construct a Japanese instructiondataset by expanding and filtering existing datasets and apply the dataset to aJapanese pre-trained base model. We performed Low-Rank Adaptation (LoRA) tuningon both Japanese and English existing models using our instruction dataset. Weevaluated these models from both quantitative and qualitative perspectives. Asa result, the effectiveness of Japanese instruction datasets is confirmed. Theresults also indicate that even with relatively small LLMs, performances indownstream tasks would be improved through instruction tuning. Our instructiondataset, tuned models, and implementation are publicly available online.

From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models