mlsanityData to 7 Oct 2026

Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval

arXiv 2411.06237 ↗First posted 9 Nov 2024cs.IR cs.LGcited by 0

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematically extracting data from the university official webpage and employing advanced prompt engineering techniques, we generate accurate, contextually relevant responses to user queries. We developed a comprehensive university benchmark, UniversityQuestionBench (UQB), to rigorously evaluate our system performance, based on common key metrics in the filed of RAG pipelines, assessing accuracy and reliability through various metrics and real-world scenarios. Our experimental results demonstrate significant improvements in the precision and relevance of generated responses, enhancing user experience and reducing the time required to obtain relevant answers. In summary, this paper presents a novel application of RAG pipelines and LLMs, supported by a meticulously prepared university benchmark, offering valuable insights into advanced AI techniques for academic data retrieval and setting the stage for future research in this domain.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 24 references that are in the map. First posted on arXiv 9 Nov 2024.

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15 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Benchmarking Large Language Models in Retrieval-Augmented Generation4 Sep
  2. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering6 Oct

Other references · 13

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  7. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  8. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  9. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Modelscited by 77
  10. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 55
  11. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
  12. 2024Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximizationcited by 29
1 more
  1. 2023Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generationcited by 14

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  1. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  2. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  3. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  4. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  5. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  6. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
  7. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  8. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation