The impressive development of large language models (LLMs) is expanding into
the realm of large multimodal models (LMMs), which incorporate multiple types
of data beyond text. However, the nature of multimodal models leads to
significant expenses in the creation of training data. Furthermore,
constructing multilingual data for LMMs presents its own set of challenges due
to language diversity and complexity. Therefore, in this study, we propose two
cost-effective methods to solve this problem: (1) vocabulary expansion and
pretraining of multilingual LLM for specific languages, and (2) automatic and
elaborate construction of multimodal datasets using GPT4-V. Based on015 these
methods, we constructed a 91K English-Korean-Chinese multilingual, multimodal
training dataset. Additionally, we developed a bilingual multimodal model that
exhibits excellent performance in both Korean and English, surpassing existing
approaches.

我们提出了两种解决多模态模型创建训练数据方面的问题的有效方法，并基于这些方法构建了一个 91K 的英 - 韩 - 中多语言多模态训练数据集，并开发了一种双语多模态模型，在韩语和英语方面表现出优秀的性能，超过了现有方法。