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

• URL: http://arxiv.org/abs/2406.04528v1
• Date: Thu, 6 Jun 2024 22:01:59 GMT
• Title: llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models
• Authors: Fabián Villena, Luis Miranda, Claudio Aracena,
• Abstract summary: This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with large language models (LLMs) llmNER can compose prompts, query the model, and parse the completion returned by the LLM. We validated our software on two NER tasks to show the library's flexibility.
• Score: 1.1196013962698619
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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