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In this paper, we introduce Kun, a novel approach for creating high-qualityinstruction-tuning datasets for large language models (LLMs) without relying onmanual annotations. Adapting a self-training algorithm based on instructionback-translation and answer polishment, Kun leverages unlabelled data fromdiverse sources such as Wudao, Wanjuan, and SkyPile to generate a substantialdataset of over a million Chinese instructional data points. This approachsignificantly deviates from traditional methods by using a self-curationprocess to refine and select the most effective instruction-output pairs. Ourexperiments with the 6B-parameter Yi model across various benchmarksdemonstrate Kun's robustness and scalability. Our method's core contributionslie in its algorithmic advancement, which enhances data retention and clarity,and its innovative data generation approach that substantially reduces thereliance on costly and time-consuming manual annotations. This methodologypresents a scalable and efficient solution for improving theinstruction-following capabilities of LLMs, with significant implications fortheir application across diverse fields. The code and dataset can be found athttps://github.com/Zheng0428/COIG-Kun

Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation