The paper introduces AraToken, an Arabic-optimized tokenizer based on the SentencePiece Unigram algorithm that incorporates a normalization pipeline to handle Arabic-specific orthographic variations. Experiments show that AraToken achieves 18% lower fertility compared to unnormalized baselines. The Language Extension Pipeline (LEP) is introduced to integrate AraToken into Qwen3-0.6B, reducing evaluation loss from 8.28 to 2.43 within 800 training steps on 100K Arabic samples. Why it matters: This research provides an efficient tokenizer tailored for Arabic, improving performance of LLMs on Arabic text and benefiting Arabic NLP research by providing released resources.
The paper introduces AraModernBERT, an adaptation of the ModernBERT encoder architecture for Arabic, focusing on transtokenized embedding initialization and long-context modeling up to 8,192 tokens. Transtokenization is shown to be crucial for Arabic language modeling, significantly enhancing masked language modeling performance. The model demonstrates stable and effective long-context modeling, improving intrinsic language modeling performance at extended sequence lengths. Why it matters: This research provides practical insights for adapting modern encoder architectures to Arabic and other languages using Arabic-derived scripts, advancing Arabic NLP.
The paper introduces ArabianGPT, a suite of transformer-based language models designed specifically for Arabic, including versions with 0.1B and 0.3B parameters. A key component is the AraNizer tokenizer, tailored for Arabic script's morphology. Fine-tuning ArabianGPT-0.1B achieved 95% accuracy in sentiment analysis, up from 56% in the base model, and improved F1 scores in summarization. Why it matters: The models address the gap in native Arabic LLMs, offering better performance on Arabic NLP tasks through tailored architecture and tokenization.
This paper explores the impact of tokenization strategies and vocabulary sizes on Arabic language model performance across NLP tasks like news classification and sentiment analysis. It compares four tokenizers, finding that Byte Pair Encoding (BPE) with Farasa performs best overall due to its morphological analysis capabilities. The study surprisingly found limited impact of vocabulary size on performance with fixed model sizes, challenging assumptions about vocabulary size and model performance. Why it matters: The findings provide insights for developing more effective and nuanced Arabic language models, particularly for handling dialectal variations and promoting responsible AI development in the region.