- Large Language Models (LLMs) and prompt-based heuristics are being used for off-the-shelf solutions to various NLP problems.
- LLM-based few-shot methods have shown promise but lag in Named Entity Recognition (NER) compared to other methods.
- « PromptNER » is introduced as a new algorithm for few-shot and cross-domain NER.
- PromptNER needs entity definitions and few-shot examples for a new NER task.
- PromptNER uses LLM to generate potential entities and explanations for their compatibility with entity type definitions.
- PromptNER achieves state-of-the-art performance in few-shot NER on ConLL, GENIA, and FewNERD datasets.
- It also outperforms previous methods in Cross Domain NER, setting new records on 3 out of 5 CrossNER domains with an average F1 gain of 3%.