Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models

• URL: http://arxiv.org/abs/2503.23714v1
• Date: Mon, 31 Mar 2025 04:28:38 GMT
• Title: Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
• Authors: Youmi Ma, Sakae Mizuki, Kazuki Fujii, Taishi Nakamura, Masanari Ohi, Hinari Shimada, Taihei Shiotani, Koshiro Saito, Koki Maeda, Kakeru Hattori, Takumi Okamoto, Shigeki Ishida, Rio Yokota, Hiroya Takamura, Naoaki Okazaki,
• Abstract summary: We build state-of-the-art instruction-tuning datasets sourced from human-written instructions.<n>LLMs fine-tuned on our datasets consistently outperform those fine-tuned on existing ones.<n>Analyses suggest that instruction-tuning in a new language allows LLMs to follow instructions, while the tuned models exhibit a notable lack of culture-specific knowledge in that language.
• Score: 22.16558378953053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized solely from LLMs, raising a fundamental question: Do we still need human-originated signals for instruction tuning? This work answers the question affirmatively: we build state-of-the-art instruction-tuning datasets sourced from human-written instructions, by simply pairing them with LLM-generated responses. LLMs fine-tuned on our datasets consistently outperform those fine-tuned on existing ones. Our data construction approach can be easily adapted to other languages; we build datasets for Japanese and confirm that LLMs tuned with our data reach state-of-the-art performance. Analyses suggest that instruction-tuning in a new language allows LLMs to follow instructions, while the tuned models exhibit a notable lack of culture-specific knowledge in that language. The datasets and fine-tuned models will be publicly available. Our datasets, synthesized with open-weight LLMs, are openly distributed under permissive licenses, allowing for diverse use cases.

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