Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation

TL;DR

This paper presents a hybrid annotation method combining human effort with Large Language Models to improve NER dataset quality, reduce noise, and address class imbalance, resulting in better model performance cost-effectively.

Contribution

It introduces a novel hybrid annotation approach that leverages LLMs and human input, along with a label mixing strategy to mitigate class imbalance in NER datasets.

Findings

Superior performance over traditional annotation methods

Effective noise reduction in manual annotations

Cost-efficient high-performance NER dataset creation

Abstract

In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional methodologies for annotating datasets for NER models are challenged by high costs and variations in dataset quality. This research introduces a novel hybrid annotation approach that synergizes human effort with the capabilities of Large Language Models (LLMs). This approach not only aims to ameliorate the noise inherent in manual annotations, such as omissions, thereby enhancing the performance of NER models, but also achieves this in a cost-effective manner. Additionally, by employing a label mixing strategy, it addresses the issue of class imbalance encountered in LLM-based annotations. Through an analysis across multiple datasets, this method has been consistently shown to provide superior performance compared to traditional annotation methods, even under constrained budget conditions. This study illuminates the potential of leveraging LLMs to improve dataset quality, introduces a novel technique to mitigate class imbalances, and demonstrates the feasibility of achieving high-performance NER in a cost-effective way.