Adapting Large Language Models (LLMs) to specialized domains requires high-quality instruction tuning datasets, which are expensive to create through human annotation. Existing data synthesis methods focus on general-purpose tasks and fail to capture domain-specific terminology and reasoning patterns. To address this, we introduce DS$^2$-Instruct, a zero-shot framework that generates domain-specific instruction datasets without human supervision. Our approach first generates task-informed keywords to ensure comprehensive domain coverage. It then creates diverse instructions by pairing these keywords with different cognitive levels from Bloom's Taxonomy. Finally, it uses self-consistency validation to ensure data quality. We apply this framework to generate datasets across seven challenging domains, such as mathematics, finance, and logical reasoning. Comprehensive evaluation demonstrates that models fine-tuned on our generated data achieve substantial improvements over existing data generation methods.

翻译：将大语言模型（LLMs）适配到专业领域需要高质量的指令微调数据集，而通过人工标注创建此类数据成本高昂。现有的数据合成方法主要关注通用任务，难以捕捉领域特定的术语和推理模式。为解决这一问题，我们提出了DS$^2$-Instruct，一种无需人工监督即可生成领域特定指令数据集的零样本框架。我们的方法首先生成任务导向的关键词以确保全面的领域覆盖。随后，通过将这些关键词与布鲁姆分类法中不同认知层级的任务相结合，生成多样化的指令。最后，采用自洽性验证机制以保证数据质量。我们将该框架应用于数学、金融和逻辑推理等七个具有挑战性的领域以生成数据集。综合评估表明，基于我们生成的数据进行微调的模型，相较于现有数据生成方法取得了显著性能提升。