SLIMER-IT: Zero-Shot NER on Italian Language
Authors: name: Andrew Zamai, name: Leonardo Rigutini, name: Marco Maggini, name: Andrea Zugarini

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.