The development of large language models has predominantly catered to English and Latin-based languages, leaving a gap in native models for languages with complex morphologies like Arabic. The research detailed in ‘ArabianGPT: Native Arabic GPT-based Large Language Model’ introduces ArabianGPT, part of the ArabianLLM series, purpose-built to grasp the nuances of the Arabic language.

ArabianGPT’s innovative aspects include:

• Native Arabic Focus: Designed specifically for Arabic’s unique linguistic structure.
• AraNizer Tokenizer: A tailored tokenizer that effectively handles Arabic morphology.
• Model Variants: Different sizes to accommodate varied computational needs.
• Improved Task Performance: Fine-tuning led to significant accuracy boosts in tasks like sentiment analysis.

The empirical results from fine-tuning ArabianGPT models for NLP tasks show major improvements over the base models, underlining the importance of specialized models for languages with intricate features. This advancement could lead to more accurate and nuanced language technologies for Arabic-speaking populations, enhancing communication and information accessibility