Summary of Can Llm Substitute Human Labeling? a Case Study Of Fine-grained Chinese Address Entity Recognition Dataset For Uav Delivery, by Yuxuan Yao et al.
Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
by Yuxuan Yao, Sichun Luo, Haohan Zhao, Guanzhi Deng, Linqi Song
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary CNER-UAV is a fine-grained Chinese Name Entity Recognition (NER) dataset designed for address resolution in Unmanned Aerial Vehicle (UAV) delivery systems. The dataset comprises five categories, allowing comprehensive training and evaluation of NER models. To create this dataset, researchers sourced data from a real-world UAV system, cleaned and desensitized it to ensure privacy and integrity, and annotated 12,000 samples with human experts and Large Language Model assistance. Classical NER models were evaluated on the dataset, providing in-depth analysis. The dataset and models are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new dataset is created to help computers recognize Chinese names and addresses used in Unmanned Aerial Vehicle delivery systems. This dataset has five categories of information and was made by cleaning and preparing data from a real-world delivery system. It includes over 12,000 labeled samples that were checked by humans and AI experts. The researchers tested different computer models on this dataset to see how well they work. All the data and code are available for anyone to use. |
Keywords
» Artificial intelligence » Large language model » Ner
