mlsanityData to 14 Oct 2026

NepaliGPT: A Generative Language Model for the Nepali Language

arXiv 2506.16399 ↗First posted 19 Jun 2025cs.CL cs.AIcited by 2

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Abstract (arXiv)

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 textitNepaliGPT, 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%.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 19 Jun 2025.

Builds on

3 of 13 references.

References · 3

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
  3. 2020A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languagescited by 131

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Citing papers per year: 2026: 2.

2 papers.