title: SLIMER-IT: Zero-Shot NER on Italian Language
summary: 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.
paper_authors._id: 66f3c35b3c412b900bd2bb02
paper_authors.name: Andrew Zamai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-25T18:24:16.438Z
paper_authors._id: 65be17eb52edc430281c6a72
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65be17eb52edc430281c6a72/ZeSxgA25KnJ8pqvvy6rdl.png
paper_authors.fullname: Andrew Zamai
paper_authors.type: user
paper_authors.user: andrewzamai
paper_authors._id: 66f3c35b3c412b900bd2bb03
paper_authors.name: Leonardo Rigutini
paper_authors._id: 66f3c35b3c412b900bd2bb04
paper_authors.name: Marco Maggini
paper_authors._id: 66f3c35b3c412b900bd2bb05
paper_authors.name: Andrea Zugarini
paper_title: SLIMER-IT: Zero-Shot NER on Italian Language
paper_summary: 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.
paper_ai_summary: An evaluation framework and instruction-tuned model for zero-shot named entity recognition in Italian outperform existing state-of-the-art models on unseen entity types.
paper_ai_keywords: Named Entity Recognition BIO sequence labeling Large Language Models Zero-Shot NER SLIMER instruction-tuning prompts evaluation framework