In recent years, the rise of large language models (LLMs) has made it possible to directly achieve named entity recognition (NER) without any demonstration samples or only using a few samples through in-context learning (ICL). However, standard ICL only helps LLMs understand task instructions, format and input-label mapping, but neglects the particularity of the NER task itself. In this paper, we propose a new prompting framework P-ICL to better achieve NER with LLMs, in which some point entities are leveraged as the auxiliary information to recognize each entity type. With such significant information, the LLM can achieve entity classification more precisely. To obtain optimal point entities for prompting LLMs, we also proposed a point entity selection method based on K-Means clustering. Our extensive experiments on some representative NER benchmarks verify the effectiveness of our proposed strategies in P-ICL and point entity selection.

本文提出了一种新的提示框架P-ICL，通过利用一些点实体作为辅助信息实现更精确的大型语言模型（LLMs）实体分类，并提出了基于K-Means聚类的点实体选择方法。在一些代表性的命名实体识别基准测试中，我们的实验证实了P-ICL和点实体选择策略的有效性。

P-ICL：大型语言模型的点上下文学习用于命名实体识别