AIBullisharXiv – CS AI · 10h ago6/10
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Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Researchers demonstrate that large language models can match or exceed fine-tuned BERT performance on Named Entity Recognition tasks when provided with hundreds of in-context examples rather than just a few. The study shows many-shot in-context learning can also serve as a data annotation framework, generating high-quality training data that improves low-resource NER by ~10% F1 when used to fine-tune supervised models.