Methodology of Adapting Large English Language Models for Specific Cultural Contexts
<|reference_start|>Methodology of Adapting Large English Language Models for Specific Cultural Contexts: 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.<|reference_end|>
@article{zhang2024methodology,
    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},
    archivePrefix={arXiv},
    eprint={2406.18192},
    primaryClass={cs.CL cs.AI}
}