Retrieval-Augmented Generation

Also known as RAG

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from documents, databases, or other sources at answer time, improving access to knowledge that may be missing or outdated in model parameters.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

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

Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval
Mar 18, 2026Md. Asraful Haque, Aasar Mehdi, Maaz Mahboob +1LLM Hallucination Mitigation LLM Grounding
Computational Unit, Z.H. College of Engineering & Technology, Aligarh Muslim University, India · Interdisciplinary Center for Artificial Intelligence, Aligarh Muslim University, India · Department of Computer Engineering, Aligarh Muslim University, India
FACE-net: Factual Calibration and Emotion Augmentation for Retrieval-enhanced Emotional Video Captioning
Mar 18, 2026Weidong Chen, Cheng Ye, Zhendong Mao +5Video Captioning Retrieval-Augmented Generation
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230027, China · School of Computer Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation
Mar 18, 2026Chaeyoung Huh, Hyunmin Hwang, Jung Hwan Shin +3Retrieval-Augmented Generation Clinical Decision Support
1Korea Advanced Institute of Science and Technology, KAIST, Republic of Korea · Department of Neurology, Seoul National University Hospital, Republic of Korea · 3Asan Medical Center, University of Ulsan, College of Medicine, Republic of Korea +1
IndexRAG: Index-Time Reasoning for Multi-Hop Retrieval-Augmented Generation
Mar 17, 2026Zhenghua Bao, Yi ShiMulti-Hop QA Retrieval-Augmented Generation
Continuum AI Shanghai, China · Continuum AI San Francisco, CA, USA
Open-Source Reproduction and Explainability Analysis of Corrective Retrieval Augmented Generation
Mar 17, 2026Surya Vardhan YalavarthiRetrieval-Augmented Generation RAG Robustness
College of Engineering and Applied Science, University of Cincinnati
AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval
Mar 17, 2026Shuvam Banerji Seal, Aheli Poddar, Alok Mishra +1Retrieval-Augmented Generation Information Retrieval
Indian Institute of Science Education and Research, Kolkata, India · Institute of Engineering & Management, Kolkata, India
CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
Mar 17, 2026Tianyi Huang, Ying Kai DengFactual QA Question Answering
Ryquo · App-In Club
Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation
Mar 16, 2026Renjie Liang, Yiling Ma, Yang Xing +6Healthcare Multimodal RAG
University of Florida · Yale University · University of Southern California
Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models
Mar 16, 2026Francesco Sovrano, Alberto BacchelliFaithfulness of Language Model Explanations Retrieval-Augmented Generation
Collegium Helveticum, ETH Zurich, Zurich, Switzerland · University of Zurich, Zurich, Switzerland
The Impact of Ideological Discourses in RAG: A Case Study with COVID-19 Treatments
Mar 16, 2026Elmira Salari, Maria Claudia Nunes Delfino, Hazem Amamou +4Political Bias in Language Models Retrieval-Augmented Generation
Wichita State University · Pontifícia Universidade Católica de São Paulo · Institut national de la recherche scientifique +1
P $^2$ RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top- $k$ Retrieval
Mar 16, 2026Yulong Ming, Mingyue Wang, Jijia Yang +4Privacy-Preserving RAG RAG Security
City University of Hong Kong · Peng Cheng Laboratory
Test-Time Strategies for More Efficient and Accurate Agentic RAG
Mar 12, 2026Brian Zhang, Deepti Guntur, Zhiyang Zuo +7Retrieval-Augmented Generation Agentic RAG
University of Massachusetts Amherst · Adobe Research
Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
Mar 12, 2026Yaocong Li, Qiang Lan, Leihan Zhang +1Retrieval-Augmented Generation Legal NLP
School of Economics and Management, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China · College of Computing, Beijing Information Science and Technology University, No. 12 Xiaoying East Road, Haidian District, Beijing, 100085, China
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
Mar 12, 2026Jihao Zhao, Daixuan Li, Pengfei Li +3Document Chunking Retrieval-Augmented Generation
School of Information, Renmin University of China Beijing, China · School of Smart Governance, Renmin University of China Suzhou, China · School of Information, BRAIN, Renmin University of China Beijing, China +1
Can Small Language Models Use What They Retrieve? An Empirical Study of Retrieval Utilization Across Model Scale
Mar 12, 2026Sanchit PandeyRetrieval-Augmented Generation Knowledge Augmentation for Language Models
BITS Pilani, Hyderabad, India · BITS Pilani, Hyderabad Campus, Telangana, India
Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning
Mar 12, 2026Seung hee Choi, MinJu Jeon, Hyunwoo Oh +2Video Saliency Prediction Dense Video Captioning
Hanyang University
Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval
Mar 11, 2026Andrea Volpini, Elie Raad, Beatrice Gamba +1Structured RAG Retrieval-Augmented Generation
WordLift, Rome, Italy
R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment
Mar 11, 2026Zhuangzi Li, Jian Jin, Shilv Cai +1Image Quality Assessment Vision-Language Models
College of Computing and Data Science, Nanyang Technological University, Singapore
AILS-NTUA at SemEval-2026 Task 8: Evaluating Multi-Turn RAG Conversations
Mar 11, 2026Dimosthenis Athanasiou, Maria Lymperaiou, Giorgos Filandrianos +2Retrieval-Augmented Generation RAG Evaluation
National Technical University of Athens
Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation
Mar 10, 2026Eeham Khan, Luis Rodriguez, Marc QueudotRAG Citation Faithfulness Retrieval-Augmented Generation
Concordia University, Montréal, QC, Canada · Centre de recherche informatique de Montréal (CRIM), Montréal, QC, Canada
MITRA: An AI Assistant for Knowledge Retrieval in Physics Collaborations
Mar 10, 2026Abhishikth Mallampalli, Sridhara DasuKnowledge-Intensive QA AI for Science
University of Wisconsin-Madison
Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG
Mar 10, 2026Jan Drole, Ana Gjorgjevikj, Barbara Korouši'c Seljak +1Entity Linking Language Model Robustness
Computer Systems Department, Jožef Stefan International Postgraduate School, Ljubljana, Slovenia · Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
Mar 10, 2026Jiashuo Sun, Yixuan Xie, Jimeng Shi +2Structured RAG Retrieval-Augmented Generation
University of Illinois Urbana-Champaign
Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back
Mar 10, 2026Renwei MengAgentic RAG Retrieval-Augmented Generation
Anhui University, Hefei, China
PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integration
Mar 9, 2026Abdul Rehman Akbar, Samuel Wales-McGrath, Alejadro Levya +5Biomedical IR Computational Pathology
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA · Department of Pathology and Laboratory Medicine, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA · PathPresenter
SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation
Mar 9, 2026Yagiz Can Akay, Muhammed Yusuf Kartal, Esra Alparslan +2Multi-Document QA Retrieval-Augmented Generation
TOBB University of Economics and Technology · OSTIM Technical University
Gradually Excavating External Knowledge for Implicit Complex Question Answering
Mar 9, 2026Chang Liu, Xiaoguang Li, Lifeng Shang +4Iterative RAG Question Answering
The University of Hong Kong · Huawei Noah’s Ark Lab
Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation
Mar 8, 2026David Beauchemin, Richard KhouryLLM Evaluation LLM Reliability
Group for Research in Artificial Intelligence of Laval University (GRAIL), Université Laval, Quebec City, Quebec, Canada
KohakuRAG: A simple RAG framework with hierarchical document indexing
Mar 8, 2026Shih-Ying Yeh, Yueh-Feng Ku, Ko-Wei Huang +1Retrieval-Augmented Generation Structured RAG
National Tsing Hua University · Comfy Org Research · Kohaku-Lab
Upgrade the Retriever First: What an LLM Call per Chunk Buys in RAG
Mar 8, 2026Bhavik ManglaDocument Chunking Retrieval-Augmented Generation
Independent Research

Mar 18, 2026J/K move · Enter open · S save

Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval

Md. Asraful Haque, Aasar Mehdi, Maaz Mahboob, Tamkeen Fatima

Computational Unit, Z.H. College of Engineering & Technology, Aligarh Muslim University, India · Interdisciplinary Center for Artificial Intelligence, Aligarh Muslim University, India · Department of Computer Engineering, Aligarh Muslim University, India

LLM Hallucination Mitigation LLM Grounding LLM Answer Verification Retrieval-Augmented Generation

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Large Language Models (LLMs) have achieved unprecedented fluency but remain susceptible to "hallucinations" - the generation of factually incorrect or ungrounded content. This limitation is particularly critical in high-stakes domains where reliability is paramount. We propose a domain-grounded tiered retrieval and verification architecture designed to systematically intercept factual inaccuracies by shifting LLMs from stochastic pattern-matchers to verified truth-seekers. The proposed framework utilizes a four-phase, self-regulating pipeline implemented via LangGraph: (I) Intrinsic Verification with Early-Exit logic to optimize compute, (II) Adaptive Search Routing utilizing a Domain Detector to target subject-specific archives, (III) Refined Context Filtering (RCF) to eliminate non-essential or distracting information, and (IV) Extrinsic Regeneration followed by atomic claim-level verification. The system was evaluated across 650 queries from five diverse benchmarks: TimeQA v2, FreshQA v2, HaluEval General, MMLU Global Facts, and TruthfulQA. Empirical results demonstrate that the pipeline consistently outperforms zero-shot baselines across all environments. Win rates peaked at 83.7% in TimeQA v2 and 78.0% in MMLU Global Facts, confirming high efficacy in domains requiring granular temporal and numerical precision. Groundedness scores remained robustly stable between 78.8% and 86.4% across factual-answer rows. While the architecture provides a robust fail-safe for misinformation, a persistent failure mode of "False-Premise Overclaiming" was identified. These findings provide a detailed empirical characterization of multi-stage RAG behavior and suggest that future work should prioritize pre-retrieval "answerability" nodes to further bridge the reliability gap in conversational AI.