Summary of Nuner: Entity Recognition Encoder Pre-training Via Llm-annotated Data, by Sergei Bogdanov et al.
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
by Sergei Bogdanov, Alexandre Constantin, Timothée Bernard, Benoit Crabbé, Etienne Bernard
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: This paper presents NuNER, a compact language representation model specifically designed for Named Entity Recognition (NER) tasks. By leveraging Large Language Models (LLMs), NuNER can be fine-tuned to solve downstream NER problems in a data-efficient manner, outperforming comparable-sized foundation models in the few-shot regime and rivaling larger LLMs. The pre-training dataset’s size and entity-type diversity are crucial factors in achieving good performance. This work contributes to the development of task-specific foundation models made possible by LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have created a new tool called NuNER that helps computers recognize names, dates, and other important information (named entities) in text. It uses a type of AI model called Large Language Models to learn how to do this job quickly and accurately. This new tool can be used for various tasks like identifying people, places, or events mentioned in texts. The size and variety of the training data are key factors that make NuNER good at its job. |
Keywords
* Artificial intelligence * Few shot * Named entity recognition * Ner
