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 a 4% (absolute) improvement in F1 score on the ConLL dataset, a 9% (absolute) improvement on the GENIA dataset, and a 4% (absolute) improvement on the FewNERD dataset. PromptNER also moves the state of the art on Cross Domain NER, outperforming prior methods (including those not limited to the few-shot setting), setting a new mark on 3/5 CrossNER target domains, with an average F1 gain of 3%, despite using less than 2% of the available data.

翻译：令人意外的是，大型语言模型（LLMs）与不断增多的基于提示的启发式方法相结合，如今提供了强大的现成方案，为众多经典自然语言处理问题提供了小样本解决方案。然而，尽管初期结果令人鼓舞，这些基于LLM的小样本方法在命名实体识别（NER）领域仍远未达到当前最优水平——目前主流方法包括通过端到端结构理解学习表征，并在标准标注语料上进行微调。本文提出了PromptNER，一种用于小样本和跨领域NER的最新最优算法。为适应任意新的NER任务，PromptNER除标准小样本示例外，还需一组实体类型定义。给定句子后，PromptNER通过提示LLM生成潜在实体列表及其对应解释，以论证这些实体与所提供实体类型定义的兼容性。值得注意的是，PromptNER在小样本NER任务上实现了最优性能：在ConLL数据集上F1值提升4%（绝对值），在GENIA数据集上提升9%（绝对值），在FewNERD数据集上提升4%（绝对值）。同时PromptNER推动了跨领域NER的最新进展，在3/5的CrossNER目标领域上超越了先前方法（包括不限于小样本设定的方法），平均F1值提升3%，而所用数据量不足可用数据的2%。