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.
@article{arxiv.2406.18192,
title = {Methodology of Adapting Large English Language Models for Specific Cultural Contexts},
author = {Wenjing Zhang and Siqi Xiao and Xuejiao Lei and Ning Wang and Huazheng Zhang and Meijuan An and Bikun Yang and Zhaoxiang Liu and Kai Wang and Shiguo Lian},
journal= {arXiv preprint arXiv:2406.18192},
year = {2024}
}