@inproceedings{yadav-etal-2025-prompt,
    title = "Prompt Engineering for {N}epali {NER}: Leveraging {H}indi-Capable {LLM}s for Low-Resource Languages",
    author = "Yadav, Dipendra  and
      Suravee, Sumaiya  and
      Kemnitz, Stefan  and
      Strauss, Tobias  and
      Yordanova, Kristina",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.158/",
    pages = "1364--1373",
    abstract = "This study provides a systematic evaluation of prompt engineering strategies for Named Entity Recognition in Nepali, a low-resource language with high similarity to Hindi, by leveraging Hindi-capable Meta{'}s LLaMA 3.3:70B model. Four prompting techniques{---}Baseline, Chain-of-Thought, Self-Refine, and Least-toMost{---}are assessed in both zero-shot and fewshot settings. As a novel contribution, we propose an entity-aware sentence selection strategy that prioritizes example diversity and entity coverage for few-shot prompting. Experimental results show that, without Nepali examples, zero-shot and one-shot prompts frequently yield unstructured or hallucinated outputs, underscoring the limitations of cross-lingual capabilities without in-context supervision. However, including even a small number of carefully selected Nepali examples{---}sometimes as few as ten{---}substantially enhances model performance, with the Least-to-Most approach achieving the highest F1 scores. These findings highlight the potential of prompt-based adaptation and principled example curation for extending LLM capabilities to related, low-resource languages, offering a practical alternative to full model fine-tuning."
}