Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English, leaving other languages largely behind. Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i.e., lack of large-scale, high-quality image-text data). In this work, we propose \trainname, an effective training paradigm for training large multimodal models in low-resource languages. \trainname demonstrates that \textbf{M}ultilingual language models can \textbf{P}ivot zero-shot \textbf{M}ultimodal learning across languages. Specifically, based on a strong multilingual large language model, multimodal models pretrained on English-only image-text data can well generalize to other languages in a zero-shot manner, even surpassing models trained on image-text data in native languages. Taking Chinese as a practice of \trainname, we build large multimodal models \modelname in image-to-text and text-to-image generation, which achieve state-of-the-art (open-source) performance in Chinese. To facilitate future research, we open-source codes and model weights at https://anonymous.4open.science/r/VisCPM-8E13.
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Jinyi Hu Yuan Yao Chongyi Wang SHAN WANG Yinxu Pan Qianyu Chen Tianyu Yu Hanghao Wu Yue Zhao Haoye Zhang Xu Han Yankai Lin Jiao Xue dahai li Zhiyuan Liu Maosong Sun