Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation
Joan Santoso, Patrick Sutanto, Billy Kelvianto Cahyadi, Esther Irawati Setiawan · 2024
Named Entity Recognition (NER) is an important task, but to achieve great performance, it is usually necessary to collect a large amount of labeled data, incurring high costs.In this paper, we propose using open-source Large Language Models (LLM) to generate NER data with only a few labeled examples, minimizing the need for extensive human-annotated data.Our proposed method is simple and can perform well using only a few labeled data points.Experimental results on diverse low-resource NER datasets show that our proposed data generation method can significantly improve the baseline.Additionally, our method can be used to augment datasets with class-imbalance problems and consistently improves model performance on macro-F1 metrics.