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  # SLIMER: Show Less Instruct More Entity Recognition
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- SLIMER is an instruction-tuned LLM (LLaMA-2-7B based) for NER.
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- 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.
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  <img src="https://huggingface.co/expertai/SLIMER/resolve/main/SLIMER_instruction_prompt.png" width="200">
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- SLIMER performs comparably to state-of-the-art approaches in out-of-domain zero-shot NER, while being trained on a reduced tag set.
 
 
 
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- <img src="https://huggingface.co/expertai/SLIMER/resolve/main/OOD_evals.png">
 
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  # SLIMER: Show Less Instruct More Entity Recognition
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+ SLIMER is an instruction-tuned LLM (LLaMA-2-7B based) for zero-shot NER.
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+ 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.
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  <img src="https://huggingface.co/expertai/SLIMER/resolve/main/SLIMER_instruction_prompt.png" width="200">
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+ 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.
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+ 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.
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+ <img src="https://huggingface.co/expertai/SLIMER/resolve/main/OOD_evals.png">
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