LEGALMIDM: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model

Youngjoon Jang, Chanhee Park, Hyeonseok Moon, Young-kyoung Ham, jiwon moon, Jinhyeon Kim, JuKyung Jung, Heuiseok Lim

Keywords: Data Curation, Synthetic Data, Data Mixture, Domain Adaptation, Legal LLM

Abstract: In recent years, the rapid proliferation of open-source large language models (LLMs) has spurred efforts to turn general-purpose models into domain specialists. However, many domain-specialized LLMs are developed using datasets and training protocols that are not aligned with the nuanced requirements of real-world applications. In the legal domain, where precision and reliability are essential, this lack of consideration limits practical utility. In this study, we propose a systematic training framework grounded in the practical needs of the legal domain, with a focus on Korean law. We introduce LEGALMIDM, a Korean legal-domain LLM, and present a methodology for constructing high-quality, use-case-driven legal datasets and optimized training pipelines. Our approach emphasizes collaboration with legal professionals and rigorous data curation to ensure relevance and factual accuracy, and demonstrates effectiveness in key legal tasks.

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Submission Number: 59