Shot NER

Shot NER (Named Entity Recognition) focuses on identifying named entities in text with limited labeled data (few-shot) or even without any labeled data (zero-shot). Current research heavily utilizes large language models (LLMs), adapting them through techniques like prompt engineering, instruction tuning, and contrastive learning to improve performance. These methods aim to overcome the limitations of traditional NER approaches that require extensive training data, thereby improving the efficiency and generalizability of entity recognition across diverse domains and languages. This work has significant implications for various NLP applications, enabling more robust and adaptable information extraction from text with limited resources.