Mi:dm 2.0 Korea-centric Bilingual Language Models
Donghoon Shin, Sejung Lee, Soonmin Bae, Hwijung Ryu, Changwon Ok, Hoyoun Jung, Hyesung Ji, Jeehyun Lim, Jehoon Lee, Ji-Eun Han, Jisoo Baik, Mihyeon Kim, Riwoo Chung, Seongmin Lee, Wonjae Park, Yoonseok Heo, Youngkyung Seo, Seyoun Won, Boeun Kim, Cheolhun Heo, Eunkyeong Lee

TL;DR
Mi:dm 2.0 is a Korea-centric bilingual large language model that incorporates Korean cultural values and reasoning, achieving state-of-the-art results and supporting diverse applications through high-quality data and specialized architecture.
Contribution
The paper introduces Mi:dm 2.0, a culturally aligned Korean bilingual LLM with innovative data processing, a custom tokenizer, and two optimized configurations for general and resource-constrained use.
Findings
State-of-the-art performance on Korean benchmarks
Top zero-shot results on KMMLU
Strong evaluation across language and social science tasks
Abstract
We introduce Mi:dm 2.0, a bilingual large language model (LLM) specifically engineered to advance Korea-centric AI. This model goes beyond Korean text processing by integrating the values, reasoning patterns, and commonsense knowledge inherent to Korean society, enabling nuanced understanding of cultural contexts, emotional subtleties, and real-world scenarios to generate reliable and culturally appropriate responses. To address limitations of existing LLMs, often caused by insufficient or low-quality Korean data and lack of cultural alignment, Mi:dm 2.0 emphasizes robust data quality through a comprehensive pipeline that includes proprietary data cleansing, high-quality synthetic data generation, strategic data mixing with curriculum learning, and a custom Korean-optimized tokenizer to improve efficiency and coverage. To realize this vision, we offer two complementary configurations:…
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Taxonomy
TopicsBig Data and Digital Economy · Topic Modeling · Computational and Text Analysis Methods
