Retrieval-Augmented Generation

Also known as RAG

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Overview

Retrieval-augmented generation (RAG) studies how to give language models relevant information from documents, databases, or other sources at answer time, improving access to knowledge that may be missing or outdated in model parameters. Common approaches combine dense or sparse search with hybrid retrieval, reranking, and document chunking; GraphRAG and agentic methods add structure or iterative search for multi-hop questions and long documents. Work spans open-domain QA benchmarks as well as specialized settings such as legal, medical, code, and multimodal retrieval. A central tension is that more retrieved context can improve evidence coverage but also introduce noise, conflicts, cost, and unsupported answers, so systems increasingly focus on selective retrieval, verification, citation fidelity, and realistic evaluation.

Latest papers

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796 papers72 papers in the last four weeks, down 14% on the four weeks before. 0.4% of all new papers.

  1. The Chameleon Nature of LLMs: Quantifying Multi-Turn Stance Instability in Search-Enabled Language Models

    Oct 19, 2025Shivam Ratnakar, Sanjay RaghavendraRetrieval-Augmented GenerationLLM Evaluation

    University of Southern California

  2. RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data

    Oct 9, 2025Mohammad BaqarSoftware EngineeringAutomated Software Engineering

    Cisco Systems Inc, CA, USA

  3. Eigen-Agent: Adaptive Multi-Agent Scientific Reasoning with Monitor-Based RAG

    Sep 25, 2025Xiangru Tang, Wanghan Xu, Yujie Wang +13Scientific Reasoning in Language ModelsMulti-Agent Reasoning

    Yale University · Shanghai Jiao Tong University · Fudan University +4

  4. Freshness and the Limits of Heuristic Trend Detection in Temporal RAG

    Sep 20, 2025Matthew GrofskyRetrieval-Augmented GenerationTopic Modeling

    Independent Security Researcher

  5. Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG

    Sep 17, 2025Harshad Khadilkar, Abhay GuptaRetrieval-Augmented GenerationGraph Retrieval-Augmented Generation

    Indian Institute of Technology Bombay · Indian Institute of Technology Patna

  6. Enhancing Knowledge Density via Hierarchical Context Pruning for Retrieval-Augmented Repository-Level Code Completion

    Aug 13, 2025Xiaohan Chen, Xiang Chen, Yu Tian +3LLM Code GenerationRetrieval-Augmented Generation

    MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics · Tsinghua University

  7. Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: A Comparative Study

    Jul 11, 2025Marina Luketina, Andrea Benkel, Christoph G. SchuetzAI-Assisted Decision MakingLegal Reasoning

    University of Applied Sciences Upper Austria – Campus Steyr, Wehrgrabengasse 1–3, Steyr, 4400, Austria · Johannes Kepler University Linz, Altenberger Str. 69, Linz, 4040, Austria

  8. ππ-CoT: Prolog-Initialized Chain-of-Thought Prompting for Multi-Hop Question-Answering

    Jun 25, 2025Chao Wan, Albert Gong, Mihir Mishra +3Multi-Hop QALLM Prompting

    Cornell University.

  9. KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG

    Jun 3, 2025Yongjian Li, HaoCheng Chu, Yukun Yan +7Retrieval-Augmented GenerationLLM Alignment

    School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University · 2Tsinghua Laboratory of Brain and Intelligence · School of Informatics, Xiamen University +6

  10. Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family

    Apr 25, 2025Pierre-Carl Langlais, Pavel Chizhov, Mattia Nee +6Retrieval-Augmented GenerationLLM Grounding

    PleIAs, Paris, France

  11. Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

    Apr 14, 2025Yujing Wang, Jinwen Chen, Hainan Zhang +5Retrieval-Augmented GenerationPrivacy Leakage in Language Models

    Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence, Beihang University, China Beijing, China

  12. ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

    Feb 21, 2025Pengcheng Huang, Zhenghao Liu, Yukun Yan +8Retrieval-Augmented GenerationKnowledge Conflicts in Language Models

    School of Computer Science and Engineering, Northeastern University, China · Department of Computer Science and Technology, Institute for AI, Tsinghua University, China · Microsoft Research Asia, Beijing, China +1

  13. XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

    Dec 20, 2024Qili Zhang, Qianren Mao, Yangyifei Luo +15Retrieval-Augmented GenerationRAG Evaluation

    †Zhongguancun Laboratory · ∗Beihang University · ‡Nanyang Technological University +1

  14. Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation

    Nov 23, 2024Junhyeok Lee, Yujin Oh, Dahyoun Lee +9Radiology Report GenerationRetrieval-Augmented Generation

    Cancer Biology, Seoul National University College of Medicine, Korea · Radiology, Massachusetts General Hospital and Harvard Medical School, USA · Biomedical Systems Informatics, Yonsei University College of Medicine, Korea +10

  15. Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations

    Oct 14, 2024Garima Agrawal, Sashank Gummuluri, Cosimo SperaRetrieval-Augmented GenerationCustomer Support Automation

    Minerva CQ, Sunnyvale, California, USA

  16. HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA

    Feb 1, 2024Xinyue Chen, Pengyu Gao, Jiangjiang Song +1Retrieval-Augmented GenerationMulti-Document QA

    1Nanjing University of Aeronautics and Astronautics. · 2Southeast University, Nanjing, Jiangsu, China.