Make context easier: enhancing fine-tuned NER models via LLM-based text rewriting

Purpose This study aims to enhance the performance of fine-tuned small named entity recognition (NER) models by leveraging the language generation capability of large language models (LLMs) to rewrite entity contexts, addressing the limited performance gains of state-of-the-art (SoTA) NER methods in recent years. Design/methodology/approach The authors propose a novel paradigm that uses LLMs to modify the surrounding context of entities in the input text, transforming hard cases into easier ones for the NER model to recognize. Experiments are conducted on multiple widely used NER data sets to evaluate the effectiveness of the proposed method. No additional retraining or architectural modifications are applied to the vanilla NER models. Findings Experimental results demonstrate that the method consistently improves the performance of existing NER models across all tested data sets, achieving SoTA results. The approach effectively mitigates challenges associated with out-of-vocabulary entities, syntactically complex sentences and linguistic distractors while maintaining relatively low computational cost. Originality/value This work introduces a new paradigm for NER that integrates LLM text rewriting with pretrained small NER models. Unlike previous approaches relying on data augmentation or NER model retraining, the authors’ method achieves performance gains purely through LLM-based context rewriting, offering a cost-efficient and scalable solution for real-world applications.