mlsanityData to 7 Oct 2026

Retrieval-Augmented Generation for AI-Generated Content: A Survey

arXiv 2402.19473 ↗First posted 29 Feb 2024cs.CVcited by 352

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

Abstract (arXiv)

Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC). Despite its notable successes, AIGC still faces hurdles such as updating knowledge, handling long-tail data, mitigating data leakage, and managing high training and inference costs. Retrieval-Augmented Generation (RAG) has recently emerged as a paradigm to address such challenges. In particular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios. We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators. This unified perspective encompasses all RAG scenarios, illuminating advancements and pivotal technologies that help with potential future progress. We also summarize additional enhancements methods for RAG, facilitating effective engineering and implementation of RAG systems. Then from another view, we survey on practical applications of RAG across different modalities and tasks, offering valuable references for researchers and practitioners. Furthermore, we introduce the benchmarks for RAG, discuss the limitations of current RAG systems, and suggest potential directions for future research. Github: https://github.com/PKU-DAIR/RAG-Survey.

Key facts. Cited by 352 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 287 of its 411 references that are in the map. First posted on arXiv 29 Feb 2024.

Builds on

287 of 411 references.

Closest ancestors · 17

Same direction, newest first

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  2. 2024CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models30 Jan
  3. 2024Corrective Retrieval Augmented Generation29 Jan
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  5. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan
  6. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generator15 Jan
  7. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System11 Jan
  8. 2024Reinforcement Learning for Optimizing RAG for Domain Chatbots10 Jan
  9. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  10. 2023RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation16 Dec
  11. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems16 Nov
  12. 2023Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation7 Nov
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  3. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory3 May
  4. 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation28 Sep
  5. 2022A Survey on Retrieval-Augmented Text Generation2 Feb

Other references · 270

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50 most cited of 270 All 270 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 103; 2025: 155; 2026: 94.

352 papers.

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  7. 2025MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilotcited by 83
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