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    • DDDT_A_204787 2343..2355

    Yanfei Wei,Beibei Lv,Jinling Xie, Y. Zhang,Yuning Lin,Shengshan Wang, Jing Zhong,Yongxin Chen, Yue Peng, Jing Ma

    semanticscholar

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    Abstract
    Yanfei Wei* Beibei Lv* Jinling Xie* Yuan Zhang Yuning Lin Shengshan Wang Jing Zhong Yongxin Chen Yue Peng Jing Ma 1Department of Physiology, Guangxi University of Chinese Medicine, Nanning, Guangxi 530200, People’s Republic of China; 2Medical Science Experimental Center, Guangxi University of Traditional Chinese Medicine, Nanning, Guangxi 530200, People’s Republic of China; 3Department of State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Nankai University, Tianjin 710032, People’s Republic of China
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
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