Many-shot ICL with LLMs matches or exceeds supervised BERT on NER and generates high-quality labels for low-resource settings, producing ~10% absolute F1 gains when used to fine-tune BERT.
Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SAM-NER improves cross-domain zero-shot NER by discovering entities, projecting them into domain-invariant semantic archetypes, and then calibrating those archetypes to target labels with a frozen LLM.
citing papers explorer
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Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Many-shot ICL with LLMs matches or exceeds supervised BERT on NER and generates high-quality labels for low-resource settings, producing ~10% absolute F1 gains when used to fine-tune BERT.
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SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition
SAM-NER improves cross-domain zero-shot NER by discovering entities, projecting them into domain-invariant semantic archetypes, and then calibrating those archetypes to target labels with a frozen LLM.