Narrative-UFET shows that adding controlled synthetic narrative context improves ultra-fine entity typing on long-tail types over sentence-level baselines, with type-changing narratives providing stronger gains than natural contexts.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing
Narrative-UFET shows that adding controlled synthetic narrative context improves ultra-fine entity typing on long-tail types over sentence-level baselines, with type-changing narratives providing stronger gains than natural contexts.
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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.