CultureLLM is a cost-effective solution to incorporate cultural differences into large language models (LLMs). The method uses the World Value Survey (WVS) as seed data and generates semantically equivalent training data through semantic data augmentation. By fine-tuning culture-specific LLMs and a unified model (CultureLLM-One) on 9 cultures, including both high- and low-resource languages, CultureLLM significantly outperforms existing models like GPT-3.5 and Gemini Pro, achieving performance comparable to or better than GPT-4. Human studies show that the generated samples are semantically equivalent to the original ones, providing an effective solution for LLM augmentation. The approach involves three steps: sampling, semantic data augmentation, and fine-tuning. The semantic data augmentation method generates diverse and semantically equivalent training data, which helps improve the model's cultural understanding. Extensive experiments on 60 culture-related datasets demonstrate that CultureLLM performs well across various tasks, including offensive language detection, hate speech detection, and spam detection. The model also shows resistance to catastrophic forgetting and supports open-source fine-tuning. The research highlights the importance of addressing cultural bias in LLMs and provides a practical solution for improving cultural awareness in AI systems.CultureLLM is a cost-effective solution to incorporate cultural differences into large language models (LLMs). The method uses the World Value Survey (WVS) as seed data and generates semantically equivalent training data through semantic data augmentation. By fine-tuning culture-specific LLMs and a unified model (CultureLLM-One) on 9 cultures, including both high- and low-resource languages, CultureLLM significantly outperforms existing models like GPT-3.5 and Gemini Pro, achieving performance comparable to or better than GPT-4. Human studies show that the generated samples are semantically equivalent to the original ones, providing an effective solution for LLM augmentation. The approach involves three steps: sampling, semantic data augmentation, and fine-tuning. The semantic data augmentation method generates diverse and semantically equivalent training data, which helps improve the model's cultural understanding. Extensive experiments on 60 culture-related datasets demonstrate that CultureLLM performs well across various tasks, including offensive language detection, hate speech detection, and spam detection. The model also shows resistance to catastrophic forgetting and supports open-source fine-tuning. The research highlights the importance of addressing cultural bias in LLMs and provides a practical solution for improving cultural awareness in AI systems.