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arXiv · 2507.06795

ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining

Seonwu Kim·Yohan Na·Kihun Kim·Hanhee Cho·Geun Lim·Mintae Kim·Seongik Park·Ki Hyun Kim·Youngsub Han·Byoung-Ki Jeon
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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.

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Seonwu Kim, Yohan Na, Kihun Kim, Hanhee Cho, Geun Lim, Mintae Kim, Seongik Park, Ki Hyun Kim, Youngsub Han, Byoung-Ki Jeon. 2025-07-09. ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining. https://arxiv.org/abs/2507.06795

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