Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
<|reference_start|>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 \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{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 \textbf{L}arge \textbf{L}anguage \textbf{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}.<|reference_end|>
@article{yao2024can,
    title={Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese
  Address Entity Recognition Dataset for UAV Delivery},
    author={Yuxuan Yao, Sichun Luo, Haohan Zhao, Guanzhi Deng, Linqi Song},
    journal={arXiv preprint arXiv:2403.06097},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06097},
    primaryClass={cs.CL cs.AI cs.IR}
}