LLMs, such as ChatGPT and GPT-4, have dramatically transformed natural language processing research and shown promising strides towards AGI. Nonetheless, the high costs associated with training and deploying LLMs present substantial obstacles to transparent, accessible academic research. While several LLMs, such as LLaMA, have been open-sourced by the community, these predominantly focus on English corpora, limiting their usefulness for other languages. In this paper, we propose a method to augment LLaMA with capabilities for understanding and generating Chinese text and its ability to follow instructions. We achieve this by extending LLaMA's existing vocabulary with an additional 20,000 Chinese tokens, thereby improving its encoding efficiency and semantic understanding of Chinese. We further incorporate secondary pre-training using Chinese data and fine-tune the model with Chinese instruction datasets, significantly enhancing the model's ability to comprehend and execute instructions. Our experimental results indicate that the newly proposed model markedly enhances the original LLaMA's proficiency in understanding and generating Chinese content. Additionally, the results on the C-Eval dataset yield competitive performance among the models with several times the size of ours. We have made our pre-trained models, training scripts, and other resources available through GitHub, fostering open research for our community. Chinese LLaMA series: \url{https://github.com/ymcui/Chinese-LLaMA-Alpaca} and Chinese Llama-2 series: \url{https://github.com/ymcui/Chinese-LLaMA-Alpaca-2}
The paper presents advancements in adapting LLMs for Chinese language through vocabulary extension, low-rank adaptation, and secondary pre-training.
Chinese LLaMA and Chinese Alpaca models were developed and fine-tuned using instruction-following datasets to enhance performance in specific Chinese language tasks.
Experimental results show significant improvements in benchmarks for Chinese language tasks, demonstrating the effectiveness of the proposed methods and implications for broader multilingual applications.
The paper titled "Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca" presents a significant advancement in adapting LLMs for Chinese language understanding and generation. This work aims to address the limitations of existing LLMs, including LLaMA, which are primarily trained on English-centric corpora, thereby lacking efficient support for Chinese language tasks.
The authors propose a comprehensive approach to enhance LLaMA’s proficiency with Chinese text by undertaking several key steps:
The enhanced models, Chinese LLaMA and Chinese Alpaca, were evaluated through various benchmarks:
Instruction Following Tasks:
C-Eval Benchmark:
Quantization Impact:
The methodology proposed in this paper offers a blueprint for extending existing LLMs to better serve underrepresented languages by improving vocabulary support and leveraging low-rank adaptations for efficient training. The successful application of these techniques demonstrates the potential for applying similar strategies to other languages, promoting greater inclusivity and utility of LLMs across diverse linguistic contexts.
Theoretical Contributions:
Practical Contributions:
This paper delineates a methodical approach to significantly enhance the Chinese language capabilities of LLaMA and Alpaca models. By extending the vocabulary, employing effective adaptation techniques like LoRA, and fine-tuning with comprehensive datasets, the authors have set a precedent for adapting LLMs to non-English languages. The results achieved in various benchmarks underscore the potential of these models and provide a path forward for future advancements in multilingual natural language processing.