Jul 2026· IEEE International Conference on Circuits and Systems for Communications· pp. 1-6· 0 citations· 20 references
Abstract
Readability assessment for Arabic remains challenging due to the language's complex morphology. This paper presents a comparative study benchmarking traditional Machine Learning (ML), advanced Deep Learning (DL), and finetuned Large Language Models (LLMs). Utilizing a dataset of 4,519 Arabic sentences categorized into three proficiency levels, we evaluate models across accuracy and computational efficiency. Our results demonstrate that a hybrid CNN-BiLSTM architecture utilizing AraVec (Word2Vec) embeddings achieves a peak accuracy of 96.68%, outperforming fine-tuned LLMs like Llama3.2-1B (94.69%). We provide empirical evidence of the prohibitive resource demands in LLMs, which required significantly higher training times (14,697s) compared to specialized DL models (162.98). These findings suggest that for discrete Arabic text classification, tailored DL architectures provide a superior balance of precision and resource efficiency.
This study integrates parallel multi-kernel word-level convolutional features into conventional and hybrid deep learning models for Arabic text analysis tasks, providing a systematic within-study assessment of model sensitivity to architecture, preprocessing, and learning-rate selection.
Ahmed I.Taloba, George Samy Rady, Khaled F. Hussain· International Journal of Adv...· 0 citations
An important practical reproducible framework for multilingual RAG benchmarking and insights for optimizing performance on resource-constrained devices are contributed and support the latent language hypothesis by suggesting an internal model bias toward high-resource languages.
B. J. Mohd, Khalil M. Ahmad Yousef, Salah G. Abu Ghalyon· Language Resources and Evalu...· 0 citations
A two-stage deep learning pipeline that combines Arabic text stemming and morphological classification within a single deep learning architecture is proposed that demonstrates a 38% improvement over the best traditional stemmer, P-Stemmer.
Azal Alaswaad, B. Minaei-Bidgoli· Bulletin of Electrical Engin...· 0 citations
An in-depth evaluation of instruction tuning for Arabic NLP tasks using three prominent LLMs: LLaMA 3.1-8B, AceGPT-v2-8B, and Qwen3-8B shows that instruction tuning consistently improves performance across most tasks, with notable variations in effectiveness across different tasks and prompts.
Maged Saeed Al-shaibani, Zaid Alyafeai, Irfan Ahmad· Language Resources and Evalu...· 0 citations
This paper proposes a general optimization framework that combines a vocabulary pruning method with a targeted fine-tuning protocol for MNMT models, and reduces the vocabulary size from over 128,000 to approximately 10,000 tokens, enabling a 60% memory saving without any loss in performance.
