Assessment of Generative Named Entity Recognition in the Era of Large Language Models
This study evaluated open-source Large Language Models (LLMs) for Named Entity Recognition (NER) tasks, comparing them to traditional models. Researchers found that using parameter-efficient fine-tuning and structured output formats like inline brackets or XML allows LLMs to achieve performance competitive with traditional models. The paper concludes that LLM NER capability comes from instruction following and generation, not just memorization.