mlsanityData to 7 Oct 2026

PersianRAG: A Retrieval-Augmented Generation System for Persian Language

arXiv 2411.02832 ↗First posted 5 Nov 2024cs.CL cs.AI cs.IRcited by 0

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

Abstract (arXiv)

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 25 of its 33 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

25 of 33 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions3 Oct
  2. 2024Exploring Retrieval Augmented Generation in Arabic14 Aug
  3. 2024Retrieval-augmented generation in multilingual settings1 Jul
  4. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models17 Apr
  5. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documents23 Mar
  6. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers22 Mar
  7. 2024CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models30 Jan
  8. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 17

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  7. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  8. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  9. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  10. 2023SONAR: Sentence-Level Multimodal and Language-Agnostic Representationscited by 105
  11. 2019Neural Arabic Question Answeringcited by 48
  12. 2024Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generationcited by 23
5 more
  1. 2019Arabic natural language processing: An overviewcited by 20
  2. 2023PersianLLaMA: Towards Building First Persian Large Language Modelcited by 14
  3. 2023Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Modelscited by 11
  4. 2024PersianMind: A Cross-Lingual Persian-English Large Language Modelcited by 7
  5. 2024Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Languagecited by 4

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  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  5. 2024Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval
  6. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  7. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  8. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation