LTNER: Large Language Model Tagging for Named Entity Recognition with
  Contextualized Entity Marking
Faren Yan Peng Yu Xin Chen
The use of LLMs for natural language processing has become a popular trend in
the past two years, driven by their formidable capacity for context
comprehension and learning, which has inspired a wave of research from
academics and industry professionals. However, for certain NLP tasks, such as
NER, the performance of LLMs still falls short when compared to supervised
learning methods. In our research, we developed a NER processing framework
called LTNER that incorporates a revolutionary Contextualized Entity Marking
Gen Method. By leveraging the cost-effective GPT-3.5 coupled with context
learning that does not require additional training, we significantly improved
the accuracy of LLMs in handling NER tasks. The F1 score on the CoNLL03 dataset
increased from the initial 85.9% to 91.9%, approaching the performance of
supervised fine-tuning. This outcome has led to a deeper understanding of the
potential of LLMs.