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arXiv · 2406.18192

Methodology of Adapting Large English Language Models for Specific Cultural Contexts

Wenjing Zhang·Siqi Xiao·Xuejiao Lei·Ning Wang·Huazheng Zhang·Meijuan An·Bikun Yang·Zhaoxiang Liu·Kai Wang·Shiguo Lian
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Abstract

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.

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Wenjing Zhang, Siqi Xiao, Xuejiao Lei, Ning Wang, Huazheng Zhang, Meijuan An, Bikun Yang, Zhaoxiang Liu, Kai Wang, Shiguo Lian. 2024-06-27. Methodology of Adapting Large English Language Models for Specific Cultural Contexts. https://arxiv.org/abs/2406.18192

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