Show Less, Instruct More: Enriching Prompts with Definitions and
  Guidelines for Zero-Shot NER
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.
6683ae31549c1b932cac7473
Andrew Zamai
admin_assigned
2024-07-03T09:48:19.723Z
65be17eb52edc430281c6a72
https://cdn-avatars.huggingface.co/v1/production/uploads/65be17eb52edc430281c6a72/ZeSxgA25KnJ8pqvvy6rdl.png
Andrew Zamai
user
andrewzamai
6683ae31549c1b932cac7474
Andrea Zugarini
admin_assigned
2024-07-03T09:48:25.743Z
6426a5c798a5be164d38ae44
https://cdn-avatars.huggingface.co/v1/production/uploads/6426a5c798a5be164d38ae44/3crh3dtGViXB_xlDW_WeM.jpeg
Andrea Zugarini
user
azugarini
6683ae31549c1b932cac7475
Leonardo Rigutini
admin_assigned
2024-07-03T09:48:42.972Z
652e3f87656cca7ce9013007
/avatars/fb4f0f58dc29d10fbe6b55751eac1398.svg
Leonardo Rigutini
user
leonardorigutini
6683ae31549c1b932cac7476
Marco Ernandes
admin_assigned
2024-07-03T09:48:49.599Z
63c10809894342c896485ddd
/avatars/533307c0f2d2780804e4442aeb0d5bff.svg
Marco Ernandes
user
MarcoErnandes
6683ae31549c1b932cac7477
Marco Maggini
admin_assigned
2024-07-03T09:48:56.732Z
653e274b78214ec5d5e0e4a3
/avatars/700ed89b53c1733010d0eb0fb40eccfa.svg
Marco Maggini
user
magginim
Show Less, Instruct More: Enriching Prompts with Definitions and
  Guidelines for Zero-Shot NER
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.
SLIMER enhances performance in zero-shot NER for unseen entities by using fewer examples and enriched prompts, matching state-of-the-art results with a reduced tag set.
Large Language Models Named Entity Recognition zero-shot NER out-of-domain fine-tuning entity classes never-seen-before definition guidelines prompt engineering