@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Prompt Engineering for Nepali NER: Leveraging Hindi-Capable LLMs for Low-Resource Languages
%A Yadav, Dipendra
%A Suravee, Sumaiya
%A Kemnitz, Stefan
%A Strauss, Tobias
%A Yordanova, Kristina
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F yadav-etal-2025-prompt
%X 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.
%U https://aclanthology.org/2025.ranlp-1.158/
%P 1364-1373
Markdown (Informal)
[Prompt Engineering for Nepali NER: Leveraging Hindi-Capable LLMs for Low-Resource Languages](https://aclanthology.org/2025.ranlp-1.158/) (Yadav et al., RANLP 2025)
ACL