Open Access

A Survey of Retrieval-Augmented Language Models for Knowledge-Intensive Text Applications

https://doi.org/10.55640/ijmcsit-v03i09-06

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Mr. Sachin Manekar ¹ ,

⁴ Assistant Professor, Department of Computer Sciences and Applications, Mandsaur University, Mandsaur, India

Abstract

Retrieval-Augmented Language Models (RALMs) have emerged as an effective approach for addressing the limitations of conventional language models in knowledge-intensive text applications. These models combine external knowledge retrieval and language generation, enabling them to deliver more relevant, accurate, and contextually appropriate responses and alleviate the need for internal knowledge. This survey reviews the fundamental concepts, architecture, knowledge sources, retrieval techniques, and major types of Retrieval-Augmented Generation (RAG) systems. It explores how the retrieval-based language generation process is affected by textual documents, scientific literature, databases, knowledge bases, enterprise documents and multimodal sources. The survey also covers the use of RAG for knowledge-intensive tasks, such as question answering, reasoning, document analysis, summarization, and information extraction. Particularly, emerging applications in healthcare and education, where reliable and domain-specific knowledge retrieval is a necessity, are given special attention. Besides, the survey stresses on some challenges related to retrieval quality, knowledge freshness, contextual relevance, hallucination, and system scalability. Last, future research directions on enhancing the robustness, efficiency, reliability and domain adaptability of RALMs are discussed.

Keywords

Artificial Intelligence, Retrieval-Augmented Generation (RAG), Knowledge Retrieval

References

S. Devarakonda, R. Lingam, and V. Challa, “Confidence-Gated RAG for Adaptive Retrieval in Sequential Agents,” in ICLR 2026 Workshop on Logical Reasoning of Large Language Models, ICLR, 2026, pp. 01–10, Apr.

Y. Gao et al., “Retrieval-Augmented Generation for Large Language Models : A Survey,” pp. 1–21, 2024.

W. Fan et al., “A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 6491–6501. doi: 10.1145/3637528.3671470.

C. P. Singh, “Enterprise-Grade AI-Driven Text Analytics and Insight Extraction Using Transformer-Based NLP Models,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/ISDFS69419.2026.11459059.

S. Gupta, R. Ranjan, and S. N. Singh, “A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions,” no. Ml, 2024, doi: 10.48550/arXiv.2410.12837.

M. N. Paralath, “Bridging Astronomical Imagery and Text with Advanced Multi-Modal AI,” in 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA), IEEE, Nov. 2025, pp. 1–8. doi: 10.1109/ICCCA66364.2025.11325649.

S. K. Kota, “Retrieval-Augmented Generation (RAG) Systems: Architectures, Strategies, and Evaluation,” pp. 350–358, 2024, doi: 10.32996/jcsts.

Y. Gong, P. He, H. Zhao, N. Duan, and C. Engineering, “Query Rewriting for Retrieval-Augmented Large Language Models,” 2023. doi: 10.48550/arXiv.2305.14283.

B. Sarmah, B. Hall, and S. Pasquali, “HybridRAG : Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction,” 2024.

T. Manohar and M. G.priyanka, “Document Retrieval System Using Rag,” Int. J. Data Sci. IoT Manag. Syst., vol. 5, pp. 1–12, 2026, doi: 10.64751/ijdim.2026.v5.n3.1123.

S. Mohammed, “From Sparse to Dense and Beyond: A Literature Review of Retrieval-Augmented Generation,” 2025.

Y. Wang, Evaluating Sparse and Dense Retrieval in Retrieval-Augmented Generation Systems : A Study, vol. 1, no. 1. Association for Computing Machinery, 2024. doi: 10.1145/3708657.3708747.

S. Xu, Z. Yan, and C. Dai, “MEGA-RAG : a retrieval-augmented generation framework with multi-evidence guided answer refinement for mitigating hallucinations of LLMs in public health”.

B. Ojokoh and E. Adebisi, “A Review of Question Answering Systems,” J. Web Eng., vol. 17, pp. 717–758, 2019, doi: 10.13052/jwe1540-9589.1785.

B. Zhao, Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment, vol. 1, no. 1. Association for Computing Machinery. doi: 10.1145/3746252.3761103.

H. Onozeki, “Knowledge-Grounded Dialogue Systems for Generating Interesting and Engaging Responses,” 2024. doi: 10.18653/v1/2024.yrrsds-1.9.

S. K. Singh, A. Kumar, and D. M. Sharma, “Content Summarizer And Document Gpt Using Rag,” vol. 13, no. 6, pp. 99–104, 2026.

M. Mittal and V. A. J. Raja, “Ai-Driven Clinical Decision Support Systems: Evaluating Impact On Diagnosis And Treatment Accuracy,” Int. J. Eng. Appl. Artif. Intell., vol. 3, no. 1, pp. 10–29, Jun. 2025, doi: 10.34218/IJEAAI_03_01_002.

F. Neha, D. Bhati, and D. K. Shukla, “Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review,” vol. 6, no. 9, 2025, doi: 10.3390/ai6090226.

P. S. Papageorgiou et al., “The Role of Large Language Models in Improving Diagnostic-Related Groups Assignment and Clinical Decision Support in Healthcare Systems: An Example from Radiology and Nuclear Medicine,” Appl. Sci., vol. 15, no. 16, pp. 1–15, 2025, doi: 10.3390/app15169005.

M. Alkhalaf, P. Yu, M. Yin, and C. Deng, “Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records,” J. Biomed. Inform., vol. 156, p. 104662, 2024, doi: 10.1016/j.jbi.2024.104662.

E. Asgari et al., “A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation,” npj Digit. Med., vol. 8, no. 1, p. 274, 2025, doi: 10.1038/s41746-025-01670-7.

P. Sundaramoorthy and J. Lingam, “Enhancing Supply Chain Resilience and Efficiency with Large Language Models,” in 2026 IEEE 18th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, Jun. 2026, pp. 367–373. doi: 10.1109/CICN70047.2026.11594388.

Z. Li, Z. Wang, W. Wang, K. Hung, H. Xie, and F. Lee, “Computers and Education : Artificial Intelligence Retrieval-augmented generation for educational application : A systematic survey,” Comput. Educ. Artif. Intell., vol. 8, no. May, p. 100417, 2025, doi: 10.1016/j.caeai.2025.100417.

N. Malali, “The Role of Retrieval-Augmented Generation (RAG) in Financial Document Processing: Automating Compliance and Reporting,” Int. J. Manag. Technol., vol. 12, no. 3, pp. 26–46, 2025, doi: 10.37745/ijmt.2013/vol12n32646.

I. Ahmad, A. Amelio, M. Aracne, L. Caroprese, E. Z. Gill, and C. Morbidoni, “Technologies, Applications, and Challenges of Large Language Models,” in 2026 49th MIPRO ICT and Electronics Convention (MIPRO), 2026, pp. 2116–2121. doi: 10.1109/MIPRO70003.2026.11591567.

Y. Zhang, J. Zeng, J. Wang, Y. Gao, and W. Wu, “Optimization-Oriented Retrieval-Augmented Generation for Large-Scale Document Understanding,” in 2025 International Conference on Graphics and Signal Processing (ICGSP), 2025, pp. 86–93. doi: 10.1109/ICGSP66091.2025.11379732.

S. Kiruba, C. Sam Anish, R. S. B. Krishna, and S. J. Rayen, “A Comprehensive Study-Retrieval-Augmented Generation(RAG),” in 2025 10th International Conference on Smart Structures and Systems (ICSSS), 2025, pp. 1–8. doi: 10.1109/ICSSS66939.2025.11346240.

G. Agrawal, T. Kumarage, Z. Alghamdi, and H. Liu, “Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation,” in 2024 2nd International Conference on Foundation and Large Language Models (FLLM), 2024, pp. 607–611. doi: 10.1109/FLLM63129.2024.10852457.

Z. Wang and Y.-C. Tam, “Suffix Retrieval-Augmented Language Modeling,” in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5. doi: 10.1109/ICASSP49357.2023.10096450.

A. Long et al., “Retrieval Augmented Classification for Long-Tail Visual Recognition,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 6949–6959. doi: 10.1109/CVPR52688.2022.00683.

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