@inproceedings{li-etal-2025-mosaic,
    title = "Mosaic-{IT}: Cost-Free Compositional Data Synthesis for Instruction Tuning",
    author = "Li, Ming  and
      Chen, Pei  and
      Wang, Chenguang  and
      Zhao, Hongyu  and
      Liang, Yijun  and
      Hou, YuPeng  and
      Liu, Fuxiao  and
      Zhou, Tianyi",
    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/acl25-workshop-ingestion/2025.findings-acl.1297/",
    pages = "25287--25318",
    ISBN = "979-8-89176-256-5",
    abstract = "Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In this paper, we introduce Mosaic Instruction Tuning (Mosaic-IT), a human/model-free compositional data synthesis method that can efficiently create rich and diverse augmentations from existing instruction tuning data to enhance the LLMs. Mosaic-IT randomly concatenates multiple instruction data into one and trains the model to produce the corresponding responses with predefined higher-level meta-instructions to strengthen its multi-step instruction-following and format-following skills. Our extensive evaluations demonstrate a superior performance and training efficiency of Mosaic-IT, which achieves consistent performance improvements over various benchmarks and an 80{\%} reduction in training costs compared with original instruction tuning."
}