Methodology of Adapting Large English Language Models for Specific Cultural Contexts
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913406120886272 |
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| author | Zhang, Wenjing Xiao, Siqi Lei, Xuejiao Wang, Ning Zhang, Huazheng An, Meijuan Yang, Bikun Liu, Zhaoxiang Wang, Kai Lian, Shiguo |
| author_facet | Zhang, Wenjing Xiao, Siqi Lei, Xuejiao Wang, Ning Zhang, Huazheng An, Meijuan Yang, Bikun Liu, Zhaoxiang Wang, Kai Lian, Shiguo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18192 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Methodology of Adapting Large English Language Models for Specific Cultural Contexts Zhang, Wenjing Xiao, Siqi Lei, Xuejiao Wang, Ning Zhang, Huazheng An, Meijuan Yang, Bikun Liu, Zhaoxiang Wang, Kai Lian, Shiguo Computation and Language Artificial Intelligence 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. |
| title | Methodology of Adapting Large English Language Models for Specific Cultural Contexts |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.18192 |