@inproceedings{kim-etal-2025-ixi,
    title = "ixi-{GEN}: Efficient Industrial s{LLM}s through Domain Adaptive Continual Pretraining",
    author = "Kim, Seonwu  and
      Na, Yohan  and
      Kim, Kihun  and
      Cho, Hanhee  and
      Lim, Geun  and
      Kim, Mintae  and
      Park, Seongik  and
      Kim, Ki Hyun  and
      Han, Youngsub  and
      Jeon, Byoung-Ki",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.165/",
    pages = "2387--2404",
    ISBN = "979-8-89176-333-3",
    abstract = "The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deploy and maintain large-scale models. As a result, small LLMs (sLLMs) have become a practical alternative despite inherent performance limitations. While Domain Adaptive Continual Pretraining (DACP) has been explored for domain adaptation, its utility in commercial settings remains under-examined. In this study, we validate the effectiveness of a DACP-based recipe across diverse foundation models and service domains, producing DACP-applied sLLMs (ixi-GEN). Through extensive experiments and real-world evaluations, we demonstrate that ixi-GEN models achieve substantial gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment."
}