Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery

We present CNER-UAV, a fine-grained $\mathbf{\text{C}}$hinese $\mathbf{\text{N}}$ame $\mathbf{\text{E}}$ntity $\mathbf{\text{R}}$ecognition dataset specifically designed for the task of address resolution in $\mathbf{\text{U}}$nmanned $\mathbf{\text{A}}$erial $\mathbf{\text{V}}$ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and $\mathbf{\text{L}}$arge $\mathbf{\text{L}}$anguage $\mathbf{\text{M}}$odel annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at \url{https://github.com/zhhvvv/CNER-UAV}.