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

TL;DR

LegalMidm is a Korean legal-domain LLM developed through a use-case-driven training framework emphasizing collaboration with legal experts and high-quality data curation for improved legal task performance.

Contribution

The paper introduces a systematic, use-case-driven training methodology for legal LLMs, specifically tailored for Korean law, with a focus on data quality and practical applicability.

Findings

LegalMidm performs effectively on key legal tasks.

Use-case-driven dataset construction improves model relevance.

Collaboration with legal professionals enhances data quality.

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.