llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models

2024-06-06 · Fabián Villena, Luis Miranda, Claudio Aracena

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.

📄 PDF Abstract BibTeX arXiv:2406.04528

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few-shot-ner Few-shot NERIn-Context Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER Prompt Engineering

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