Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models have strong generalization capabilities. Existing LLMs mainly focus on zero-shot NER in out-of-domain distributions, being fine-tuned on an extensive number of entity classes that often highly or completely overlap with test sets. In this work instead, we propose SLIMER, an approach designed to tackle never-seen-before named entity tags by instructing the model on fewer examples, and by leveraging a prompt enriched with definition and guidelines. Experiments demonstrate that definition and guidelines yield better performance, faster and more robust learning, particularly when labelling unseen Named Entities. Furthermore, SLIMER performs comparably to state-of-the-art approaches in out-of-domain zero-shot NER, while being trained on a reduced tag set.

翻译：近年来，针对命名实体识别任务出现了若干专用指令微调大语言模型。与传统NER方法相比，这些模型展现出强大的泛化能力。现有LLM主要关注分布外场景下的零样本NER，通常在大量实体类别上进行微调，而这些类别常与测试集高度或完全重叠。本研究提出SLIMER方法，通过指导模型学习更少示例，并利用包含定义与指南的增强提示，专门应对从未见过的命名实体标签。实验表明，定义与指南能带来更好的性能表现、更快且更稳健的学习过程，尤其在标注未见命名实体时效果显著。此外，SLIMER在分布外零样本NER任务中与最先进方法性能相当，同时仅需在缩减的标签集上进行训练。