BANER：用于少样本命名实体识别的边界感知大语言模型
BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition
AI总结：
针对少样本NER中错误跨度检测与实体原型未对齐问题，提出BANER，通过边界感知对比学习和LoRAHub跨域对齐提升LLM性能，并在多个基准和架构上验证有效。
摘要：
尽管两阶段原型网络近期在少样本命名实体识别（NER）中取得了成功，但跨度检测阶段仍存在过度检测或检测不足的错误跨度，以及类型分类阶段实体原型未对齐等挑战。此外，总体而言，LLM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 Boundary-Aware LLMs for Few-Shot Named Entity Recognition（BANER）的方法，以解决这些问题。我们引入边界感知对比学习策略，增强LLM对泛化实体跨度的实体边界感知能力。此外，我们利用 LoRAHub 将目标域信息对齐到源域，从而增强自适应跨域分类能力。在多个基准上进行的大量实验表明，我们的框架优于以往方法，验证了其有效性。特别是，所提出的策略在一系列LLM架构上均表现出有效性。代码和数据发布于 https://github.com/UESTC-GQJ/BANER。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
