llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models
<|reference_start|>llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models: Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs; by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple variables. We validated our software on two NER tasks to show the library's flexibility. llmNER aims to push the boundaries of in-context learning research by removing the barrier of the prompting and parsing steps.<|reference_end|>
@article{villena2024llmner:,
    title={llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power
  of Large Language Models},
    author={Fabi'an Villena, Luis Miranda, Claudio Aracena},
    journal={arXiv preprint arXiv:2406.04528},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04528},
    primaryClass={cs.CL}
}