In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. Adapting a self-training algorithm based on instruction back-translation and answer polishment, Kun leverages unlabelled data from diverse sources such as Wudao, Wanjuan, and SkyPile to generate a substantial dataset of over a million Chinese instructional data points. This approach significantly deviates from traditional methods by using a self-curation process to refine and select the most effective instruction-output pairs. Our experiments with the 6B-parameter Yi model across various benchmarks demonstrate Kun's robustness and scalability. Our method's core contributions lie in its algorithmic advancement, which enhances data retention and clarity, and its innovative data generation approach that substantially reduces the reliance on costly and time-consuming manual annotations. This methodology presents a scalable and efficient solution for improving the instruction-following capabilities of LLMs, with significant implications for their application across diverse fields. The code and dataset can be found at https://github.com/Zheng0428/COIG-Kun

翻译：本文提出坤（Kun），一种无需依赖人工标注即可为大型语言模型（LLM）构建高质量指令微调数据集的新方法。该方法基于指令回译与答案精炼的自训练算法，利用来自悟道、万卷、SkyPile 等多源无标注数据，生成了超过百万条中文指令数据的大规模数据集。此方法通过自筛选过程来优化并选择最有效的指令-输出对，显著区别于传统范式。我们在 6B 参数的 Yi 模型上进行的多基准测试实验验证了坤方法的鲁棒性与可扩展性。本方法的核心贡献在于其算法上的改进——提升了数据保留率与清晰度，以及其创新的数据生成途径——大幅降低了对成本高昂且耗时的人工标注的依赖。该方法为提升 LLM 的指令遵循能力提供了一种可扩展且高效的解决方案，对其在跨领域应用具有重要意义。代码与数据集可在 https://github.com/Zheng0428/COIG-Kun 获取。