NepaliGPT: A Generative Language Model for the Nepali Language
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918065616191488 |
|---|---|
| author | Pudasaini, Shushanta Shakya, Aman Shrestha, Siddhartha Bhatta, Sahil Thapa, Sunil Palikhe, Sushmita |
| author_facet | Pudasaini, Shushanta Shakya, Aman Shrestha, Siddhartha Bhatta, Sahil Thapa, Sunil Palikhe, Sushmita |
| contents | After the release of ChatGPT, Large Language Models (LLMs) have gained huge popularity in recent days and thousands of variants of LLMs have been released. However, there is no generative language model for the Nepali language, due to which other downstream tasks, including fine-tuning, have not been explored yet. To fill this research gap in the Nepali NLP space, this research proposes \textit{NepaliGPT}, a generative large language model tailored specifically for the Nepali language. This research introduces an advanced corpus for the Nepali language collected from several sources, called the Devanagari Corpus. Likewise, the research introduces the first NepaliGPT benchmark dataset comprised of 4,296 question-answer pairs in the Nepali language. The proposed LLM NepaliGPT achieves the following metrics in text generation: Perplexity of 26.32245, ROUGE-1 score of 0.2604, causal coherence of 81.25\%, and causal consistency of 85.41\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NepaliGPT: A Generative Language Model for the Nepali Language Pudasaini, Shushanta Shakya, Aman Shrestha, Siddhartha Bhatta, Sahil Thapa, Sunil Palikhe, Sushmita Computation and Language Artificial Intelligence After the release of ChatGPT, Large Language Models (LLMs) have gained huge popularity in recent days and thousands of variants of LLMs have been released. However, there is no generative language model for the Nepali language, due to which other downstream tasks, including fine-tuning, have not been explored yet. To fill this research gap in the Nepali NLP space, this research proposes \textit{NepaliGPT}, a generative large language model tailored specifically for the Nepali language. This research introduces an advanced corpus for the Nepali language collected from several sources, called the Devanagari Corpus. Likewise, the research introduces the first NepaliGPT benchmark dataset comprised of 4,296 question-answer pairs in the Nepali language. The proposed LLM NepaliGPT achieves the following metrics in text generation: Perplexity of 26.32245, ROUGE-1 score of 0.2604, causal coherence of 81.25\%, and causal consistency of 85.41\%. |
| title | NepaliGPT: A Generative Language Model for the Nepali Language |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.16399 |