  Named Entity Recognition (NER) is essential in various Natural Language
Processing (NLP) applications. Traditional NER models are effective but limited
to a set of predefined entity types. In contrast, Large Language Models (LLMs)
can extract arbitrary entities through natural language instructions, offering
greater flexibility. However, their size and cost, particularly for those
accessed via APIs like ChatGPT, make them impractical in resource-limited
scenarios. In this paper, we introduce a compact NER model trained to identify
any type of entity. Leveraging a bidirectional transformer encoder, our model,
GLiNER, facilitates parallel entity extraction, an advantage over the slow
sequential token generation of LLMs. Through comprehensive testing, GLiNER
demonstrate strong performance, outperforming both ChatGPT and fine-tuned LLMs
in zero-shot evaluations on various NER benchmarks.
