Polylm: An Open Source Polyglot Large Language Model
Wei Xiangpeng, Wei Haoran, Lin Huan, Li Tianhao, Zhang Pei, Ren Xingzhang, Li Mei, Wan Yu, Cao Zhiwei, Xie Binbin, Hu Tianxiang, Li Shangjie, Hui Binyuan, Yu Bowen, Liu Dayiheng, Yang Baosong, Huang Fei, Xie Jun. Arxiv 2023
[Paper]
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby limiting their applicability and research in other languages. Consequently, we present PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning. To assess the model’s performance, we collect several existing multilingual tasks, including multilingual understanding, question answering, generation, and translation. Extensive experiments show that PolyLM surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English. Our models, alone with the instruction data and multilingual benchmark, are available at: \url{https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation}.
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