# RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing

arXiv: https://arxiv.org/abs/2404.19543 · HTML: https://mlsanity.com/paper/2404.19543/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 104 of its 133 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 30 of 104)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)

## Built on by (most cited first, 25 of 25)

- [Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks](https://mlsanity.com/paper/2407.21059/) (2024)
- [Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents](https://mlsanity.com/paper/2405.10467/) (2024)
- [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://mlsanity.com/paper/2503.18016/) (2025)
- [Surveying the MLLM Landscape: A Meta-Review of Current Surveys](https://mlsanity.com/paper/2409.18991/) (2024)
- [SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback](https://mlsanity.com/paper/2410.18141/) (2024)
- [Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models](https://mlsanity.com/paper/2408.13808/) (2024)
- [AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understanding](https://mlsanity.com/paper/2506.13589/) (2025)
- [SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2601.03979/) (2026)
- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [Affordable AI Assistants with Knowledge Graph of Thoughts](https://mlsanity.com/paper/2504.02670/) (2025)
- [A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems](https://mlsanity.com/paper/2407.01712/) (2024)
- [DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients](https://mlsanity.com/paper/2505.19538/) (2025)
- [RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines](https://mlsanity.com/paper/2506.03401/) (2025)
- [Retrieval-Augmented Feature Generation for Domain-Specific Classification](https://mlsanity.com/paper/2406.11177/) (2024)
- [A Survey of Query Optimization in Large Language Models](https://mlsanity.com/paper/2412.17558/) (2024)
- [LEKA:LLM-Enhanced Knowledge Augmentation](https://mlsanity.com/paper/2501.17802/) (2025)
- [SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache](https://mlsanity.com/paper/2505.10951/) (2025)
- [ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever](https://mlsanity.com/paper/2510.04757/) (2025)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and Acacias](https://mlsanity.com/paper/2407.11360/) (2024)
- [Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing](https://mlsanity.com/paper/2511.22584/) (2025)
- [PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling](https://mlsanity.com/paper/2607.20230/) (2026)
- [When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.16515/) (2026)
- [NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing](https://mlsanity.com/paper/2609.00604/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have catalyzed significant advancements in Natural Language Processing (NLP), yet they encounter challenges such as hallucination and the need for domain-specific knowledge. To mitigate these, recent methodologies have integrated information retrieved from external resources with LLMs, substantially enhancing their performance across NLP tasks. This survey paper addresses the absence of a comprehensive overview on Retrieval-Augmented Language Models (RALMs), both Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Understanding (RAU), providing an in-depth examination of their paradigm, evolution, taxonomy, and applications. The paper discusses the essential components of RALMs, including Retrievers, Language Models, and Augmentations, and how their interactions lead to diverse model structures and applications. RALMs demonstrate utility in a spectrum of tasks, from translation and dialogue systems to knowledge-intensive applications. The survey includes several evaluation methods of RALMs, emphasizing the importance of robustness, accuracy, and relevance in their assessment. It also acknowledges the limitations of RALMs, particularly in retrieval quality and computational efficiency, offering directions for future research. In conclusion, this survey aims to offer a structured insight into RALMs, their potential, and the avenues for their future development in NLP. The paper is supplemented with a Github Repository containing the surveyed works and resources for further study: https://github.com/2471023025/RALM_Survey.