Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
In-context learning (ICL) with large language models (LLMs) has emerged as a powerful alternative to fine-tuning for Named Entity Recognition (NER), achieving strong performance with minimal annotation and no additional training. However, prior work has shown that despite their adaptability, LLMs still lag behind fully supervised models such as fine-tuned BERT in structured tasks like NER. While existing studies on ICL for NER have mainly explored few-shot settings, the potential of scaling to hundreds of demonstrations has not been thoroughly investigated. To address this gap, we conduct a comprehensive investigation of many-shot ICL for NER and further explore its effectiveness in annot...
| Authors | Qi Zhang, Fangping Lan, Cornelia Caragea et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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