Summary of Proggen: Generating Named Entity Recognition Datasets Step-by-step with Self-reflexive Large Language Models, by Yuzhao Heng et al.
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
by Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu, Yinghao Li, Rongzhi Zhang, Chao Zhang
First submitted to arxiv on: 17 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 This paper explores a novel approach to leveraging Large Language Models (LLMs) for named entity recognition (NER) tasks, which are notoriously challenging for these models. By instructing LLMs to self-reflect on specific domains and generate domain-relevant attributes, the authors create attribute-rich training data that surpasses conventional methods. The proposed method also preempts generating entity terms and develops NER context data around them, effectively bypassing LLMs’ limitations with complex structures. Experimental results across general and niche domains demonstrate significant performance enhancements and cost-effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to use big language models in a new way. These models are really good at learning from lots of text, but they struggle with tasks like identifying specific names or dates. The authors came up with an idea to help these models by asking them to think about the topic they’re writing about and generating extra information that’s helpful for understanding the text. This helps create better training data for other machines to learn from. The results show that this approach works well across different topics and is more efficient than usual methods. |
Keywords
* Artificial intelligence * Named entity recognition * Ner
