GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models
<|reference_start|>GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models: 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.<|reference_end|>
@article{luo2024geic:,
    title={GEIC: Universal and Multilingual Named Entity Recognition with Large
  Language Models},
    author={Hanjun Luo, Yingbin Jin, Xuecheng Liu, Tong Shang, Ruizhe Chen, Zuozhu
  Liu},
    journal={arXiv preprint arXiv:2409.11022},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11022},
    primaryClass={cs.CL cs.AI}
}