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.

翻译：大型语言模型（LLMs）的快速发展已成为人工智能领域的一个显著趋势。然而，当前最先进的LLMs主要基于英语构建。当直接应用于特定文化领域的任务时，由于领域知识的缺乏以及文化价值观差异导致的误解，这些模型存在明显的局限性。为应对这一挑战，本文提出了一种针对特定文化语境的大模型快速适应方法，该方法利用基于特定文化知识与安全价值观数据的指令微调技术。以中文作为特定文化语境，并采用LLaMA3-8B作为实验用的英语LLM，评估结果表明，经过适应的LLM在领域知识和安全价值观适应性方面能力显著提升，同时保持了其原有的专业优势。