{"ID":2896557,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.06795","arxiv_id":"2507.06795","title":"ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining","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.","short_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 limitati...","url_abs":"https://arxiv.org/abs/2507.06795","url_pdf":"https://arxiv.org/pdf/2507.06795v4","authors":"[\"Seonwu Kim\",\"Yohan Na\",\"Kihun Kim\",\"Hanhee Cho\",\"Geun Lim\",\"Mintae Kim\",\"Seongik Park\",\"Ki Hyun Kim\",\"Youngsub Han\",\"Byoung-Ki Jeon\"]","published":"2025-07-09T12:30:42Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}