The rapid growth of large language models(LLMs) has emerged as a prominent
trend in the field of artificial intelligence. However, current
state-of-the-art LLMs are predominantly based on English. They encounter
limitations when directly applied to tasks in specific cultural domains, due to
deficiencies in domain-specific knowledge and misunderstandings caused by
differences in cultural values. To address this challenge, our paper proposes a
rapid adaptation method for large models in specific cultural contexts, which
leverages instruction-tuning based on specific cultural knowledge and safety
values data. Taking Chinese as the specific cultural context and utilizing the
LLaMA3-8B as the experimental English LLM, the evaluation results demonstrate
that the adapted LLM significantly enhances its capabilities in domain-specific
knowledge and adaptability to safety values, while maintaining its original
expertise advantages.

针对大型语言模型在特定文化背景下应用的挑战，本文提出了一种快速自适应方法，利用特定文化知识和安全价值数据进行指导调整。实验结果表明，适应后的语言模型在领域特定知识和适应性方面显著提升，同时保持了其原有的专业优势。