README.md

metadata

license: llama2
datasets:
  - Universal-NER/Pile-NER-type
language:
  - en
pipeline_tag: text-generation

SLIMER: Show Less Instruct More Entity Recognition

SLIMER is an instruction-tuned LLaMA-2-7B model for zero-shot NER.

Instructed on a reduced number of samples, it is designed to tackle never-seen-before Named Entity tags by leveraging a prompt enriched with a DEFINITION and GUIDELINES for the NE to be extracted.

Currently existing LLMs for NER fine-tune on an extensive number of entity classes (around 13K) and assess zero-shot NER capabilities on Out-Of-Distribution input domains. SLIMER performs comparably to these state-of-the-art approaches on OOD input domains, while being trained only a reduced number of samples and a set of NE tags that overlap in lesser degree with test set.