Methodology of Adapting Large English Language Models for Specific Cultural Contexts
This addresses the challenge of adapting AI models for non-English cultural domains, though it is incremental as it builds on existing instruction-tuning techniques.
The paper tackles the problem of large English language models having limitations in specific cultural contexts due to knowledge gaps and cultural misunderstandings, proposing an adaptation method that significantly enhances domain-specific knowledge and safety values adaptability while preserving original expertise, as demonstrated with LLaMA3-8B in Chinese 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.