  Large Language Models (LLMs) have provided a new pathway for Named Entity
Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting
methods avoid the need for training, conserve substantial computational
resources, and rely on minimal annotated data. Previous studies have achieved
comparable performance to fully supervised BERT-based fine-tuning approaches on
general NER benchmarks. However, none of the previous approaches has
investigated the efficiency of LLM-based few-shot learning in domain-specific
scenarios. To address this gap, we introduce FsPONER, a novel approach for
optimizing few-shot prompts, and evaluate its performance on domain-specific
NER datasets, with a focus on industrial manufacturing and maintenance, while
using multiple LLMs -- GPT-4-32K, GPT-3.5-Turbo, LLaMA 2-chat, and Vicuna.
FsPONER consists of three few-shot selection methods based on random sampling,
TF-IDF vectors, and a combination of both. We compare these methods with a
general-purpose GPT-NER method as the number of few-shot examples increases and
evaluate their optimal NER performance against fine-tuned BERT and LLaMA
2-chat. In the considered real-world scenarios with data scarcity, FsPONER with
TF-IDF surpasses fine-tuned models by approximately 10% in F1 score.
