Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral
Yiming Cui Xin Yao
Mixtral, a representative sparse mixture of experts (SMoE) language model,
has received significant attention due to its unique model design and superior
performance. Based on Mixtral-8x7B-v0.1, in this paper, we propose
Chinese-Mixtral and Chinese-Mixtral-Instruct with improved Chinese language
abilities by adopting further pre-training and instruction fine-tuning.
Experimental results show that our Chinese-Mixtral and Chinese-Mixtral-Instruct
successfully improve Chinese understanding and generation performance while
retaining the original English abilities. Then, we discuss several key
questions when performing language adaptation on large language models,
including the necessity of extending the language-specific vocabulary and the
choice of the initialization model (foundation model v.s. instruction model),
by providing empirical results and analysis. We also present the visualizations
of each expert to examine their importance on downstream tasks. Our resources
are publicly available through \url{https://github.com/ymcui/Chinese-Mixtral}.