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.

This study investigates the potential of cross-lingual transfer learning for Named Entity Recognition (NER) between Hindi and Nepali, two languages that, despite their linguistic similarities, face significant disparities in available resources. By leveraging multilingual BERT models, including RemBERT, BERT Multilingual, MuRIL, and DistilBERT Multilingual, the research examines whether pre-training them on a resource-rich language like Hindi can enhance NER performance in a resource-constrained language like Nepali and vice versa. The study conducts experiments in both monolingual and cross-lingual settings to evaluate the models’ effectiveness in transferring linguistic knowledge between the two languages. The findings reveal that while RemBERT and MuRIL perform well in monolingual contexts—RemBERT excelling in Hindi and MuRIL in Nepali—BERT Multilingual performs comparatively best in cross-lingual scenarios, in generalizing features across the languages. Although DistilBERT Multilingual demonstrates slightly lower performance in cross-lingual tasks, it balances efficiency with competitive results. The study underscores the importance of model selection based on linguistic and resource-specific contexts, highlighting that general-purpose models like BERT Multilingual are particularly well-suited for cross-lingual applications.