SciNLP is the first full-text entity and relation extraction benchmark for the NLP domain, built from 60 manually annotated publications and used to evaluate models and construct a domain knowledge graph.
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2 Pith papers cite this work. Polarity classification is still indexing.
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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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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.