X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment
<|reference_start|>X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment: 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.<|reference_end|>
@article{shin2024x-llava:,
    title={X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
    author={Dongjae Shin, Hyeonseok Lim, Inho Won, Changsu Choi, Minjun Kim,
  Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
    journal={https://aclanthology.org/2024.findings-naacl.158},
    year={2024},
    doi={10.18653/v1/2024.findings-naacl.158},
    archivePrefix={arXiv},
    eprint={2403.11399},
    primaryClass={cs.CL}
}