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.

翻译：尽管大语言模型（LLMs）展现出令人印象深刻的能力，但在信息抽取任务上的性能仍不尽如人意。然而，其卓越的改写能力和广泛的世界知识为改进这些任务提供了宝贵思路。本文提出$LLM-DA$——一种基于大语言模型的少样本NER任务新型数据增强技术。为克服现有数据增强方法破坏语义完整性的局限，并应对LLM生成文本固有的不确定性，我们通过从上下文和实体两个层面增强原始数据，充分利用NER任务的独有特征。该方法采用14种上下文改写策略，设计同类型实体替换方案，并通过注入噪声增强模型鲁棒性。大量实验证明，本方法能在有限数据条件下有效提升NER模型性能。进一步的分析结果更有力验证了：我们生成的数据质量优于现有其他方法。