Summary of Ltner: Large Language Model Tagging For Named Entity Recognition with Contextualized Entity Marking, by Faren Yan and Peng Yu and Xin Chen
LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking
by Faren Yan, Peng Yu, Xin Chen
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large language models (LLMs) have revolutionized natural language processing, showcasing impressive context comprehension and learning capabilities. However, their application in named entity recognition (NER) tasks has limitations when compared to supervised learning methods. To bridge this gap, we developed LTNER, a NER processing framework that leverages the GPT-3.5 LLM and a novel Contextualized Entity Marking Gen Method. By integrating cost-effective GPT-3.5 with context learning that doesn’t require additional training, we significantly improved LLM performance in NER tasks. Our approach achieved an F1 score of 91.9% on the CoNLL03 dataset, comparable to supervised fine-tuning. This breakthrough highlights the potential of LLMs in NLP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been exploring how large language models (LLMs) can be used for natural language processing tasks like identifying important words and phrases in text. While LLMs are great at understanding context, they still struggle with a specific task called named entity recognition (NER). To improve their performance, we created a new NER processing system that uses the GPT-3.5 model. By combining this model with some clever techniques for learning from context, we were able to make LLMs much better at doing NER tasks. In fact, our approach worked just as well as more traditional methods of training models on specific datasets. |
Keywords
» Artificial intelligence » F1 score » Fine tuning » Gpt » Named entity recognition » Natural language processing » Ner » Nlp » Supervised
