  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.
