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.

翻译：近年来，大语言模型的兴起使得无需任何样本或仅通过上下文学习使用少量样本即可直接实现命名实体识别成为可能。然而，标准上下文学习仅帮助大语言模型理解任务指令、格式及输入-标签映射关系，却忽略了命名实体识别任务本身的特殊性。本文提出一种新的提示框架P-ICL，通过利用点实体作为辅助信息来识别每种实体类型，从而更好地实现基于大语言模型的命名实体识别。借助这类关键信息，大语言模型能够更精确地完成实体分类。为获取最优的点实体以提示大语言模型，我们还提出一种基于K均值聚类的点实体选择方法。在多个代表性命名实体识别基准上的大量实验验证了所提出的P-ICL策略及点实体选择方法的有效性。