  Despite the impressive capabilities of large language models (LLMs), their
performance on information extraction tasks is still not entirely satisfactory.
However, their remarkable rewriting capabilities and extensive world knowledge
offer valuable insights to improve these tasks. In this paper, we propose
$LLM-DA$, a novel data augmentation technique based on LLMs for the few-shot
NER task. To overcome the limitations of existing data augmentation methods
that compromise semantic integrity and address the uncertainty inherent in
LLM-generated text, we leverage the distinctive characteristics of the NER task
by augmenting the original data at both the contextual and entity levels. Our
approach involves employing 14 contextual rewriting strategies, designing
entity replacements of the same type, and incorporating noise injection to
enhance robustness. Extensive experiments demonstrate the effectiveness of our
approach in enhancing NER model performance with limited data. Furthermore,
additional analyses provide further evidence supporting the assertion that the
quality of the data we generate surpasses that of other existing methods.
