Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models have demonstrated strong generalization capabilities. Existing LLMs primarily focus on addressing zero-shot NER on Out-of-Domain inputs, while fine-tuning 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 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 in a more fair, though certainly more challenging, setting.

翻译：近年来，针对命名实体识别任务已涌现出多种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主要关注领域外输入的零样本NER问题，并在与测试集高度或完全重叠的大量实体类别上进行微调。本研究提出SLIMER方法，通过指导模型学习更少示例，并利用包含定义与准则的增强提示，专门应对从未见过的实体标签。实验表明，定义与准则能带来更好的性能、更快速且更稳健的学习效果，尤其在标注未见命名实体时表现突出。此外，SLIMER在领域外零样本NER任务中与最先进方法性能相当，同时其训练设置虽更具挑战性，却更为公平。