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A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions

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arxiv 2410.12837 v1 pith:DQFQPTTB submitted 2024-10-03 cs.CL cs.AIcs.IR

Shailja Gupta , Rajesh Ranjan , Surya Narayan Singh This is my paper
classification cs.CLcs.AIcs.IR
keywords modelsgenerationlanguageretrievalretrieval-augmentedaddressingcomprehensivecurrent
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This paper presents a comprehensive study of Retrieval-Augmented Generation (RAG), tracing its evolution from foundational concepts to the current state of the art. RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs, addressing key limitations of LLMs. The study explores the basic architecture of RAG, focusing on how retrieval and generation are integrated to handle knowledge-intensive tasks. A detailed review of the significant technological advancements in RAG is provided, including key innovations in retrieval-augmented language models and applications across various domains such as question-answering, summarization, and knowledge-based tasks. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Furthermore, the paper examines ongoing challenges such as scalability, bias, and ethical concerns in deployment. Future research directions are proposed, focusing on improving the robustness of RAG models, expanding the scope of application of RAG models, and addressing societal implications. This survey aims to serve as a foundational resource for researchers and practitioners in understanding the potential of RAG and its trajectory in natural language processing.

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Cited by 20 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 21 citations worldwide. Full citation record

  1. PowerDAG: Supervisory Agentic AI System for Automating Distribution Grid Analysis

    eess.SY 2026-03 unverdicted novelty 7.0 of 10

    PowerDAG achieves 94-100% success on unseen distribution grid analysis queries by combining adaptive retrieval with similarity-decay cutoff and just-in-time supervision, outperforming ReAct, LangChain, and CrewAI baselines.

  2. Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory

    cs.CV 2026-07 conditional novelty 6.5 of 10

    Dual global+latent states with hierarchical episodic merging enable reflexive, low-latency long-video agents that beat iterative reasoning baselines on accuracy and efficiency.

  3. Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

    cs.IR 2026-07 conditional novelty 6.0 of 10

    Precomputing hypothetical question embeddings for each text chunk at indexing time shifts retrieval to question–question matching and improves context precision and claim recall in RAG.

  4. MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts

    cs.AI 2025-10 conditional novelty 6.0 of 10

    MHA-RAG encodes retrieved exemplars into order-invariant soft prompts via multi-head attention, claiming ~20-point effective-accuracy gains over RAG at ~10x lower inference FLOPs.

  5. ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking

    cs.IR 2025-08 conditional novelty 6.0 of 10

    ERank trains a pointwise reasoning-LLM reranker with fine-grained integer scoring and a listwise reinforcement-learning reward, reporting nDCG@10 of 40.2 on BRIGHT for its 32B variant with BM25 hybrid scoring.

  6. Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs

    cs.SE 2025-08 conditional novelty 6.0 of 10

    Multi-modal RAG (text plus UI screenshots) with reward-based polishing generates acceptance criteria from user stories that three industry experts rated near 4/5 on relevance, correctness, and understandability.

  7. LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction

    cs.AI 2025-07 conditional novelty 6.0 of 10

    JARVIS, an LLM-based HVAC question-answering framework with an Expert-LLM, a parameterized SQL builder, and bottom-up planning, outperforms a text-to-SQL baseline and its own ablations on a small expert-curated dataset.

  8. PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling

    cs.LG 2026-07 conditional novelty 5.0 of 10

    PIER augments embedding-based retrieval for lake modeling with a physics-aware stream scored by local verifiers, improving water temperature and dissolved oxygen prediction across 356 lakes.

  9. Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting

    cs.AI 2026-07 conditional novelty 5.0 of 10

    On 635 synthetic BOP applications, multi-agent Agentic RAG reaches 86.5% decision accuracy versus 77.6% single-LLM and 76.9% naive RAG, with largest gains on multi-step and missing-information cases.

  10. HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering

    cs.AI 2026-06 reject novelty 5.0 of 10

    Organizing retrieved evidence into confidence-scored hypergraph chains is reported to lift multi-hop QA accuracy, context relevance, and faithfulness by 11–30 points over vector and graph RAG baselines on five benchmarks.

  11. SEAL: Structure and Element Aware Learning to Improve Long Structured Document Retrieval

    cs.IR 2025-08 conditional novelty 5.0 of 10

    A contrastive fine-tuning recipe that adds HTML structure and element-masking signals improves long structured document retrieval, with reported MRR@10 gains of about four points on BGE-M3.

  12. Leveraging Spreading Activation for Improved Document Retrieval in Knowledge-Graph-Based RAG Systems

    cs.AI 2025-12 conditional novelty 4.0 of 10

    A spreading-activation retrieval module over an automatically constructed knowledge graph improves multi-hop QA in RAG systems, with up to 39 percentage points gain when combined with chain-of-thought retrieval.

  13. MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems

    cs.AI 2025-08 reject novelty 4.0 of 10

    The authors claim their MultiFluxAI orchestration framework achieves 95% accuracy and 0-10 ms responses by combining rule-based routing, caching, and graph knowledge stores for multi-service RAG queries.

  14. Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions

    cs.NI 2025-08 conditional novelty 4.0 of 10

    A survey that organizes agentic AI for 6G edge networks into four pillars, compactness, efficiency, knowledge and reasoning, and migration, and illustrates them with prior case studies.

  15. Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology

    cs.CL 2025-07 reject novelty 4.0 of 10

    In a 10-case rheumatology benchmark, a 46-billion-parameter model with retrieval-augmented generation scored highest on diagnosis and treatment, but the result lacks error bars and a released dataset.

  16. VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation

    cs.AR 2025-07 conditional novelty 4.0 of 10

    A new pipeline and dataset of 20,392 synthesis-checked Verilog modules for LLM fine-tuning is presented, claimed to be the largest high-quality dataset of its kind.

  17. Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation

    cs.SE 2025-06 conditional novelty 4.0 of 10

    A field study of five real-world RAG systems evaluated by 100 users, yielding user ratings and twelve engineering lessons.

  18. HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A progressive two-stage knowledge distillation framework (HKD4VLM) reports first-place F1 scores of 98.2% and 98.4% on multimodal hallucination and factuality detection, but its ablation lacks a directly fine-tuned baseline.

  19. Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning

    cs.AI 2025-05 conditional novelty 4.0 of 10

    Hyperparameter tuning of Cognee's knowledge graph pipeline yields consistent but uneven gains across three multi-hop QA benchmarks, with best configurations varying by dataset and metric.

  20. Towards End-to-End Model-Agnostic Explanations for RAG Systems

    cs.IR 2025-09 conditional novelty 3.0 of 10

    The paper combines earlier retriever and generator explainers into a single perturbation-based, model-agnostic framework for RAG, with brief user-study evaluations.

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