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arXiv 2603.12932cs.CL
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DS$^2$-Instruct: 领域特定数据合成用于大语言模型指令微调

DS$^2$-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning

Ruiyao Xu, Noelle I. Samia, Han Liu

备注：
EACL 2026 Findings
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AI总结：

DS$^2$-Instruct通过零样本方法生成领域特定指令数据集，结合任务关键词和布卢姆分类法不同认知层次，提升模型在数学、金融等七个领域性能。

摘要：

适应大语言模型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这些数据集成本高昂。现有数据合成方法侧重通用任务，无法捕捉领域特定术语和推理模式。为此，我们引入DS$^2$-Instruct，一种零样本框架，无需人工监督生成领域特定指令数据集。我们的方法首先生成任务驱动的关键词以确保全面的领域覆盖，然后通过将这些关键词与布卢姆分类法的不同认知层次配对来创建多样化的指令。最后，利用自一致性验证确保数据质量。我们将此框架应用于生成七个具有挑战性的领域数据集，如数学、金融和逻辑推理。全面评估显示，使用我们生成的数据集进行微调的模型在现有数据生成方法上实现了显著提升。

英文摘要：

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.

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。

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