@inproceedings{zhu-etal-2025-tag,
    title = "Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation",
    author = "Zhu, He  and
      Ruan, Zhiwen  and
      Su, Junyou  and
      He, Xingwei  and
      Chen, Yun  and
      Zhang, Wenjia  and
      Chen, Guanhua",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.911/",
    pages = "17708--17729",
    ISBN = "979-8-89176-256-5",
    abstract = "High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present Tag-Instruct, a novel framework that enhances instruction complexity through structured semantic compression and controlled difficulty augmentation. Unlike previous prompt-based methods operating on raw text, Tag-Instruct compresses instructions into a compact tag space and systematically enhances complexity through RL-guided tag expansion. Through extensive experiments, we show that Tag-Instruct outperforms existing instruction complexity augmentation approaches. Our analysis reveals that operating in tag space provides superior controllability and stability across different instruction synthesis frameworks."
}