SLIMER-IT: Zero-Shot NER on Italian Language
<|reference_start|>SLIMER-IT: Zero-Shot NER on Italian Language: 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.<|reference_end|>
@article{zamai2024slimer-it:,
    title={SLIMER-IT: Zero-Shot NER on Italian Language},
    author={Andrew Zamai, Leonardo Rigutini, Marco Maggini, Andrea Zugarini},
    journal={arXiv preprint arXiv:2409.15933},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15933},
    primaryClass={cs.CL cs.IR}
}