Traditional approaches to Named Entity Recognition (NER) frame the task into a BIO sequence labeling problem. Although these systems often excel in the downstream task at hand, they require extensive annotated data and struggle to generalize to out-of-distribution input domains and unseen entity types. On the contrary, Large Language Models (LLMs) have demonstrated strong zero-shot capabilities. While several works address Zero-Shot NER in English, little has been done in other languages. In this paper, we define an evaluation framework for Zero-Shot NER, applying it to the Italian language. Furthermore, we introduce SLIMER-IT, the Italian version of SLIMER, an instruction-tuning approach for zero-shot NER leveraging prompts enriched with definition and guidelines. Comparisons with other state-of-the-art models, demonstrate the superiority of SLIMER-IT on never-seen-before entity tags.

翻译：传统的命名实体识别方法将该任务构建为BIO序列标注问题。尽管这些系统在下游任务中通常表现出色，但它们需要大量标注数据，并且难以泛化到分布外的输入领域和未见过的实体类型。相比之下，大型语言模型已展现出强大的零样本能力。虽然已有若干工作针对英语的零样本命名实体识别展开研究，但在其他语言中相关工作仍较为匮乏。本文定义了一个零样本命名实体识别的评估框架，并将其应用于意大利语。此外，我们提出了SLIMER-IT，即SLIMER的意大利语版本，这是一种利用包含定义和指导原则的提示进行指令微调以实现零样本命名实体识别的方法。与其他最先进模型的比较表明，SLIMER-IT在从未见过的实体标签上具有优越性。