Summary of Augmenting Ner Datasets with Llms: Towards Automated and Refined Annotation, by Yuji Naraki et al.
Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation
by Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda, Takafumi Horie, Kotaro Yoshida, Ryotaro Shimizu, Hiroki Naganuma
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper presents a new approach to annotating datasets for Named Entity Recognition (NER) models, combining human effort with Large Language Models (LLMs). This hybrid method aims to improve dataset quality by reducing noise and enhancing performance. The approach also addresses class imbalances in LLM-based annotations using a label mixing strategy. The study demonstrates the effectiveness of this method across multiple datasets, showing superior performance compared to traditional annotation methods even under budget constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making it easier and cheaper to train computers to recognize important information like names, locations, and organizations in text. Right now, creating these datasets is a time-consuming and expensive process. The new approach uses powerful language models to help humans annotate the data, which makes it more accurate and cost-effective. This method also helps fix a problem where certain types of information are harder to recognize than others. By using this approach, computers can become better at recognizing important information without breaking the bank. |
Keywords
» Artificial intelligence » Named entity recognition » Ner
