Generative AI for Named Entity Recognition in Low-Resource Language Nepali
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913733584879616 |
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| author | Neupane, Sameer Chapagain, Jeevan Niraula, Nobal B. Koirala, Diwa |
| author_facet | Neupane, Sameer Chapagain, Jeevan Niraula, Nobal B. Koirala, Diwa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Named Entity Recognition in Low-Resource Language Nepali Neupane, Sameer Chapagain, Jeevan Niraula, Nobal B. Koirala, Diwa Computation and Language Artificial Intelligence 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. |
| title | Generative AI for Named Entity Recognition in Low-Resource Language Nepali |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.09822 |