Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components

Jumana Alsubhi; Mohammad D. Alahmadi; Ahmed Alhusayni; Ibrahim Aldailami; Israa Hamdine; Ahmad Shabana; Yazeed Iskandar; Suhayb Khayyat

arXiv · 2506.06339

Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components

Jumana Alsubhi·Mohammad D. Alahmadi·Ahmed Alhusayni·Ibrahim Aldailami·Israa Hamdine·Ahmad Shabana·Yazeed Iskandar·Suhayb Khayyat

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Abstract

Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for combining the precision of retrieval systems with the fluency of large language models. While several studies have investigated RAG pipelines for high-resource languages, the optimization of RAG components for Arabic remains underexplored. This study presents a comprehensive empirical evaluation of state-of-the-art RAG components-including chunking strategies, embedding models, rerankers, and language models-across a diverse set of Arabic datasets. Using the RAGAS framework, we systematically compare performance across four core metrics: context precision, context recall, answer faithfulness, and answer relevancy. Our experiments demonstrate that sentence-aware chunking outperforms all other segmentation methods, while BGE-M3 and Multilingual-E5-large emerge as the most effective embedding models. The inclusion of a reranker (bge-reranker-v2-m3) significantly boosts faithfulness in complex datasets, and Aya-8B surpasses StableLM in generation quality. These findings provide critical insights for building high-quality Arabic RAG pipelines and offer practical guidelines for selecting optimal components across different document types.

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Jumana Alsubhi, Mohammad D. Alahmadi, Ahmed Alhusayni, Ibrahim Aldailami, Israa Hamdine, Ahmad Shabana, Yazeed Iskandar, Suhayb Khayyat. 2025-06-01. Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components. https://arxiv.org/abs/2506.06339

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