NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
This work addresses the need for data-efficient NER models, offering a task-specific foundation model that reduces reliance on large-scale human annotations, though it is incremental in leveraging existing LLM capabilities.
The paper tackled the problem of creating a compact language model specialized for Named Entity Recognition (NER) by using LLM-annotated data for pre-training, resulting in NuNER, which outperforms similar-sized models in few-shot settings and competes with larger LLMs.
Large Language Models (LLMs) have shown impressive abilities in data annotation, opening the way for new approaches to solve classic NLP problems. In this paper, we show how to use LLMs to create NuNER, a compact language representation model specialized in the Named Entity Recognition (NER) task. NuNER can be fine-tuned to solve downstream NER problems in a data-efficient way, outperforming similar-sized foundation models in the few-shot regime and competing with much larger LLMs. We find that the size and entity-type diversity of the pre-training dataset are key to achieving good performance. We view NuNER as a member of the broader family of task-specific foundation models, recently unlocked by LLMs.