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Large Language Models (LLMs), such as ChatGPT and GPT-4, have dramaticallytransformed natural language processing research and shown promising stridestowards Artificial General Intelligence (AGI). Nonetheless, the high costsassociated with training and deploying LLMs present substantial obstacles totransparent, accessible academic research. While several large language models,such as LLaMA, have been open-sourced by the community, these predominantlyfocus on English corpora, limiting their usefulness for other languages. Inthis paper, we propose a method to augment LLaMA with capabilities forunderstanding and generating Chinese text and its ability to followinstructions. We achieve this by extending LLaMA's existing vocabulary with anadditional 20,000 Chinese tokens, thereby improving its encoding efficiency andsemantic understanding of Chinese. We further incorporate secondarypre-training using Chinese data and fine-tune the model with Chineseinstruction datasets, significantly enhancing the model's ability to comprehendand execute instructions. Our experimental results indicate that the newlyproposed model markedly enhances the original LLaMA's proficiency inunderstanding and generating Chinese content. Additionally, the results on theC-Eval dataset yield competitive performance among the models with severaltimes the size of ours. We have made our pre-trained models, training scripts,and other resources available through GitHub, fostering open research for ourcommunity. GitHub repository: https://github.com/ymcui/Chinese-LLaMA-Alpaca

Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca