The paper introduces LLM-DA, a novel data augmentation technique for few-shot named entity recognition (NER) tasks. LLM-DA leverages the rewriting capabilities and extensive world knowledge of large language models (LLMs) to generate semantically coherent and diverse augmented data. The approach involves 14 contextual rewriting strategies, entity replacements, and noise injection to enhance robustness. Extensive experiments demonstrate that LLM-DA significantly improves NER model performance with limited data, outperforming existing methods in various datasets and scenarios. The generated data is of higher quality, maintaining both diversity and controllability, and shows superior linguistic quality in terms of informativeness, readability, and grammaticality. The study also highlights the limitations of LLMs in specific domains and the importance of balancing data diversity and controllability.The paper introduces LLM-DA, a novel data augmentation technique for few-shot named entity recognition (NER) tasks. LLM-DA leverages the rewriting capabilities and extensive world knowledge of large language models (LLMs) to generate semantically coherent and diverse augmented data. The approach involves 14 contextual rewriting strategies, entity replacements, and noise injection to enhance robustness. Extensive experiments demonstrate that LLM-DA significantly improves NER model performance with limited data, outperforming existing methods in various datasets and scenarios. The generated data is of higher quality, maintaining both diversity and controllability, and shows superior linguistic quality in terms of informativeness, readability, and grammaticality. The study also highlights the limitations of LLMs in specific domains and the importance of balancing data diversity and controllability.