mlsanityData to 7 Oct 2026

Exploring Retrieval Augmented Generation in Arabic

arXiv 2408.07425 ↗First posted 14 Aug 2024cs.CL cs.AIcited by 4

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Recently, Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing, combining the strengths of retrieval-based and generation-based models to enhance text generation tasks. However, the application of RAG in Arabic, a language with unique characteristics and resource constraints, remains underexplored. This paper presents a comprehensive case study on the implementation and evaluation of RAG for Arabic text. The work focuses on exploring various semantic embedding models in the retrieval stage and several LLMs in the generation stage, in order to investigate what works and what doesn't in the context of Arabic. The work also touches upon the issue of variations between document dialect and query dialect in the retrieval stage. Results show that existing semantic embedding models and LLMs can be effectively employed to build Arabic RAG pipelines.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 26 references that are in the map. First posted on arXiv 14 Aug 2024.

Builds on

18 of 26 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Retrieval-augmented generation in multilingual settings1 Jul
  2. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models17 Apr
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  6. 2023Mistral 7Bcited by 3,065
  7. 2018Deep contextualized word representationscited by 2,285
  8. 2024Mixtral of Expertscited by 1,785
  9. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  10. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
  11. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 358
  12. 2023SONAR: Sentence-Level Multimodal and Language-Agnostic Representationscited by 105
3 more
  1. 2023Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Modelscited by 68
  2. 2019Neural Arabic Question Answeringcited by 48
  3. 2020A Panoramic Survey of Natural Language Processing in the Arab Worldcited by 45

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Componentscited by 3
  2. 2024PersianRAG: A Retrieval-Augmented Generation System for Persian Languagecited by 0
  3. 2025Transformer Tafsir at QIAS 2025 Shared Task: Hybrid Retrieval-Augmented Generation for Islamic Knowledge Question Answeringcited by 0
  4. 2026The Hrunting of AI: Where and How to Improve English Dialectal Fairnesscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language
  2. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  3. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  4. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  5. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  6. 2025Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization
  7. 2025The Cross-Lingual Cost: Retrieval Biases in RAG over Arabic-English Corpora
  8. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions