Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In this paper, we propose an approach called Boundary-Aware LLMs for Few-Shot Named Entity Recognition to address these issues. We introduce a boundary-aware contrastive learning strategy to enhance the LLM's ability to perceive entity boundaries for generalized entity spans. Additionally, we utilize LoRAHub to align information from the target domain to the source domain, thereby enhancing adaptive cross-domain classification capabilities. Extensive experiments across various benchmarks demonstrate that our framework outperforms prior methods, validating its effectiveness. In particular, the proposed strategies demonstrate effectiveness across a range of LLM architectures. The code and data are released on https://github.com/UESTC-GQJ/BANER.

翻译：尽管两阶段原型网络在少样本命名实体识别（NER）中取得了近期成功，但在跨度检测阶段仍存在过度检测/检测不足的虚假跨度问题，在类型分类阶段也存在实体原型未对齐的挑战。此外，大语言模型（LLMs）总体上尚未被证明是有效的少样本信息抽取器。本文提出一种名为边界感知大语言模型的少样本命名实体识别方法以解决这些问题。我们引入边界感知对比学习策略来增强LLM感知实体边界的能力，从而获得泛化性更强的实体跨度。此外，我们利用LoRAHub将目标领域信息对齐至源领域，从而增强自适应跨领域分类能力。在多个基准数据集上的大量实验表明，我们的框架优于现有方法，验证了其有效性。特别值得注意的是，所提出的策略在一系列LLM架构上均展现出有效性。代码与数据已发布于https://github.com/UESTC-GQJ/BANER。