Large Language Models (LLMs) have supplanted traditional methods in numerous natural language processing tasks. Nonetheless, in Named Entity Recognition (NER), existing LLM-based methods underperform compared to baselines and require significantly more computational resources, limiting their application. In this paper, we introduce the task of generation-based extraction and in-context classification (GEIC), designed to leverage LLMs' prior knowledge and self-attention mechanisms for NER tasks. We then propose CascadeNER, a universal and multilingual GEIC framework for few-shot and zero-shot NER. CascadeNER employs model cascading to utilize two small-parameter LLMs to extract and classify independently, reducing resource consumption while enhancing accuracy. We also introduce AnythingNER, the first NER dataset specifically designed for LLMs, including 8 languages, 155 entity types and a novel dynamic categorization system. Experiments show that CascadeNER achieves state-of-the-art performance on low-resource and fine-grained scenarios, including CrossNER and FewNERD. Our work is openly accessible.

本研究解决了现有基于大语言模型的命名实体识别（NER）方法性能不足及高计算资源需求的问题。通过引入生成式提取和上下文分类（GEIC）任务，并提出级联NER框架（CascadeNER），该方法在资源消耗低的同时提升了准确性。实验结果表明，CascadeNER在低资源和细粒度场景下达到最先进的性能，具有广泛的应用潜力。

GEIC：基于大型语言模型的通用多语言命名实体识别