Kun：通过指令反向翻译与答案润色实现中文自对齐
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
AI总结：
提出Kun方法，利用指令反向翻译和答案润色，从无标注数据生成超百万中文指令数据，减少人工标注，提升LLM指令遵循能力。
摘要：
在本文中，我们介绍了Kun，一种新颖的方法，用于为大型语言模型（LLMs）创建高质量的指令微调数据集，而无需依赖人工标注。Kun采用基于指令反向翻译和答案润色的自训练算法，利用来自Wudao、Wanjuan和SkyPile等多个来源的无标注数据，生成了超过一百万条中文指令数据点的大规模数据集。该方法通过自我筛选过程来精炼和选择最有效的指令-输出对，显著偏离了传统方法。我们使用6B参数的Yi模型在各种基准上的实验证明了Kun的稳健性和可扩展性。我们方法的核心贡献在于其算法进步，增强了数据保留和清晰度，以及其创新的数据生成方法，大幅减少了对昂贵且耗时的人工标注的依赖。该方法为提升LLMs的指令遵循能力提供了一种可扩展且高效的解决方案，对它们在多个领域的应用具有重要意义。代码和数据集可在https://github.com/Zheng0428/COIG-Kun获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
