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.
@article{arxiv.2604.25297,
title = {LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model},
author = {Youngjoon Jang and Chanhee Park and Hyeonseok Moon and Young-kyoung Ham and Jiwon Moon and Jinhyeon Kim and JuKyung Jung and Heuiseok Lim},
journal= {arXiv preprint arXiv:2604.25297},
year = {2026}
}