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A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

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arxiv 2405.06211 v3 pith:7AENBG3V submitted 2024-05-10 cs.CL cs.AIcs.IR

Wenqi Fan , Yujuan Ding , Liangbo Ning , Shijie Wang , Hengyun Li , Dawei Yin , Tat-Seng Chua , Qing Li This is my paper
classification cs.CLcs.AIcs.IR
keywords knowledgellmslanguagegenerationlargemodelsprovidingra-llms
verification ladder T0 review T1 audit T2 compute T3 formal
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As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: architectures, training strategies, and applications. As the preliminary knowledge, we briefly introduce the foundations and recent advances of LLMs. Then, to illustrate the practical significance of RAG for LLMs, we systematically review mainstream relevant work by their architectures, training strategies, and application areas, detailing specifically the challenges of each and the corresponding capabilities of RA-LLMs. Finally, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Memory in the Loop: In-Process Retrieval as Extended Working Memory for Language Agents

    cs.AI 2026-07 conditional novelty 6.5 of 10

    Store latency, not architecture, gates per-step memory access; in-process ~100 µs stores make memory-in-the-loop feasible and causally reduce redundant agent actions.

  2. 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.

  3. Trajectory-Aware Retrieval Agents for Temporal Decision- Making

    cs.AI 2026-07 reject novelty 5.0 of 10

    TLM reports large accuracy gains on medical and financial temporal-decision tasks by fitting linear trends to retrieved embeddings, but its monotonicity theorem is circular and its baselines omit plain fine-tuned RAG.

  4. Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety

    cs.RO 2025-09 conditional novelty 5.0 of 10

    A unified framework of secure prompting, state memory, and rule-based safety validation improves LLM-driven robot navigation under prompt injection attacks and obstacle-heavy environments, with modest real-robot verification.

  5. Hierarchical Reranking for Scalable Financial RAG System

    cs.IR 2026-07 reject novelty 4.0 of 10

    A finance-specific RAG pipeline combining table-to-JSON conversion, two-stage reranking, and long-context split-fusion reports NDCG@20=0.7918 and second place in the ICAIF '24 FinanceRAG challenge.

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    cs.CL 2025-08 conditional novelty 4.0 of 10

    A multi-agent LLM framework generates synthetic QA datasets for RAG evaluation by combining clustering-based sampling, PII pseudonymization, and QA curation, with reported diversity gains and 0.75-0.90 masking accuracy.

  7. Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Allowing an LLM more inference steps and majority-voted graph actions improves multi-hop knowledge-graph QA scores on GRBench.

  8. A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey of 80+ Deep Research systems that proposes a four-layer taxonomy (foundation models, tool use, planning, synthesis) and compares commercial and open-source implementations.

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