Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has significantly advanced Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), which involves identifying entities like person, location, and organization names in text. LLMs are especially promising for low-resource languages due to their ability to learn from limited data. However, the performance of GenAI models for Nepali, a low-resource language, has not been thoroughly evaluated. This paper investigates the application of state-of-the-art LLMs for Nepali NER, conducting experiments with various prompting techniques to assess their effectiveness. Our results provide insights into the challenges and opportunities of using LLMs for NER in low-resource settings and offer valuable contributions to the advancement of NLP research in languages like Nepali.

本研究解决了尼泊尔语这一低资源语言命名实体识别（NER）中，生成性人工智能（GenAI）模型性能未被充分评估的问题。通过实验各种提示技术，评估最先进的大型语言模型在尼泊尔语NER中的效果，发现了在低资源环境中应用LLM所面临的挑战与机遇，为尼泊尔语等语言的自然语言处理研究提供了重要贡献。

生成性人工智能在低资源语言内的命名实体识别：尼泊尔语的案例研究