GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models

• URL: http://arxiv.org/abs/2409.11022v3
• Date: Wed, 25 Sep 2024 12:33:27 GMT
• Title: GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models
• Authors: Hanjun Luo, Yingbin Jin, Xuecheng Liu, Tong Shang, Ruizhe Chen, Zuozhu Liu,
• Abstract summary: We introduce the task of generation-based extraction and in-context classification (GEIC) We then propose CascadeNER, a universal and multilingual GEIC framework for few-shot and zero-shot NER. We also introduce AnythingNER, the first NER dataset specifically designed for Large Language Models (LLMs)
• Score: 7.714969840571947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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