title: Show Less, Instruct More: Enriching Prompts with Definitions and
    Guidelines for Zero-Shot NER
summary: 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.
paper_authors._id: 6683ae31549c1b932cac7473
paper_authors.name: Andrew Zamai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T09:48:19.723Z
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paper_authors.fullname: Andrew Zamai
paper_authors.type: user
paper_authors.user: andrewzamai
paper_authors._id: 6683ae31549c1b932cac7474
paper_authors.name: Andrea Zugarini
paper_authors.status: admin_assigned
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paper_authors.fullname: Andrea Zugarini
paper_authors.type: user
paper_authors.user: azugarini
paper_authors._id: 6683ae31549c1b932cac7475
paper_authors.name: Leonardo Rigutini
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T09:48:42.972Z
paper_authors._id: 652e3f87656cca7ce9013007
paper_authors.avatarUrl: /avatars/fb4f0f58dc29d10fbe6b55751eac1398.svg
paper_authors.fullname: Leonardo Rigutini
paper_authors.type: user
paper_authors.user: leonardorigutini
paper_authors._id: 6683ae31549c1b932cac7476
paper_authors.name: Marco Ernandes
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T09:48:49.599Z
paper_authors._id: 63c10809894342c896485ddd
paper_authors.avatarUrl: /avatars/533307c0f2d2780804e4442aeb0d5bff.svg
paper_authors.fullname: Marco Ernandes
paper_authors.type: user
paper_authors.user: MarcoErnandes
paper_authors._id: 6683ae31549c1b932cac7477
paper_authors.name: Marco Maggini
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T09:48:56.732Z
paper_authors._id: 653e274b78214ec5d5e0e4a3
paper_authors.avatarUrl: /avatars/700ed89b53c1733010d0eb0fb40eccfa.svg
paper_authors.fullname: Marco Maggini
paper_authors.type: user
paper_authors.user: magginim
paper_title: Show Less, Instruct More: Enriching Prompts with Definitions and
    Guidelines for Zero-Shot NER
paper_summary: 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.
paper_ai_keywords: instruction-tuned Large Language Models (LLMs) Named Entity Recognition (NER) zero-shot NER out-of-domain distributions fine-tuning entity classes never-seen-before named entity tags prompt enriched definition and guidelines faster and more robust learning unseen Named Entities state-of-the-art approaches reduced tag set