In a surprising turn, Large Language Models (LLMs) together with a growing
arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches
providing few-shot solutions to myriad classic NLP problems. However, despite
promising early results, these LLM-based few-shot methods remain far from the
state of the art in Named Entity Recognition (NER), where prevailing methods
include learning representations via end-to-end structural understanding and
fine-tuning on standard labeled corpora. In this paper, we introduce PromptNER,
a new state-of-the-art algorithm for few-Shot and cross-domain NER. To adapt to
any new NER task PromptNER requires a set of entity definitions in addition to
the standard few-shot examples. Given a sentence, PromptNER prompts an LLM to
produce a list of potential entities along with corresponding explanations
justifying their compatibility with the provided entity type definitions.
Remarkably, PromptNER achieves state-of-the-art performance on few-shot NER,
achieving an 11% (absolute) improvement in F1 score on the ConLL dataset, and a
10% (absolute) improvement on the FewNERD dataset. PromptNER also moves the
state of the art on Cross Domain NER, outperforming all prior methods
(including those not limited to the few-shot setting), setting a new mark on
all 5 CrossNER target domains, with an average F1 gain of 9%, despite using
less than 2% of the available data.

本文介绍了 PromptNER 算法，将大型语言模型与有限训练样本相结合，实现跨领域、少量样本下的命名实体识别任务，相对于传统方法，取得了十分显著的性能提升。