@inproceedings{yeen-etal-2025-manta,
    title = "{MANTA}: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets",
    author = "Yeen, Heuiyeen  and
      Hong, Seokhee  and
      Yun, Hyeongu  and
      Lee, Jinsik",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1019/",
    doi = "10.18653/v1/2025.findings-emnlp.1019",
    pages = "18755--18770",
    ISBN = "979-8-89176-335-7",
    abstract = "We introduce MANTA, an automated pipeline that generates high-quality large-scale instruction fine-tuning datasets from massive web corpora while preserving their diversity and scalability. By extracting structured syllabi from web documents and leveraging high-performance LLMs, our approach enables highly effective query-response generation with minimal human intervention. Extensive experiments on 8B-scale LLMs demonstrate that fine-tuning on the MANTA-1M dataset significantly outperforms other massive dataset generation methodologies, particularly in knowledge-intensive tasks such as MMLU and MMLU-Pro, while also delivering superior performance across a broad spectrum of tasks. Moreover, MANTA supports seamless scalability by allowing the continuous integration of web corpus data, enabling expansion into domains requiring intensive knowledge."
}