LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

arXiv:2402.14568v1 Announce Type: new
Abstract: Despite the impressive capabilities of large language models (LLMs), their performance on information extraction tasks is still not entirely satisfactory. However, their remarkable rewriting capabilities and extensive world knowledge offer valuable insights to improve these tasks. In this paper, we propose $LLM-DA$, a novel data augmentation technique based on LLMs for the few-shot NER task. To overcome the limitations of existing data augmentation methods that compromise semantic integrity and address the uncertainty inherent in LLM-generated …

abstract arxiv augmentation capabilities cs.cl data extraction few-shot information information extraction insights knowledge language language models large language large language models llm llms paper performance recognition tasks type via world