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from documents, databases, or other sources at answer time, improving access to knowledge that may be missing or outdated in model parameters.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

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives
Apr 6, 2026Ziyi Chen, Mengxian Lyu, Cheng Peng +1Medical LLMs Clinical Language Model Evaluation
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL
RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World
Apr 6, 2026Hanbing Liu, Lang Cao, Yang LiRetrieval-Augmented Generation Continual Learning for LLMs
Tsinghua University; Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School, Tsinghua University · University of Illinois Urbana-Champaign · School of Artificial Intelligence, Chinese University of Hong Kong (Shenzhen)
This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA
Apr 6, 2026Hye Sun Yun, Geetika Kapoor, Michael Mackert +4Framing Effects in Language Models Medical LLMs
Northeastern University · UC Berkeley · UT Austin +2
Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering
Apr 6, 2026Byeolhee Kim, Min-Kyung Kim, Young-Hak Kim +1Medical QA Retrieval-Augmented Generation
Department of Information Medicine, Asan Medical Center, Seoul, Republic of Korea · Department of Computer Science, Yonsei University, Seoul, Republic of Korea · INMED DATA, Seoul, Republic of Korea +1
Graph-to-Frame RAG: Visual-Space Knowledge Fusion for Training-Free and Auditable Video Reasoning
Apr 6, 2026Songyuan Yang, Weijiang Yu, Ziyu Liu +4Video Reasoning Retrieval-Augmented Generation
National University of Defense Technology · Sun Yat-sen University
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Apr 5, 2026Deepa Tilwani, Yash Saxena, Ankur Padia +2LLM Reliability Neuro-Symbolic AI
Department of Computer Science, AI Institute, University of South Carolina, Columbia, South Carolina, USA · Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA · Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA
MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers
Apr 5, 2026Zhihan Guo, Yuting Lu, Jionghao LinRetrieval-Augmented Generation Knowledge Augmentation for Language Models
The University of Hong Kong, Hong Kong SAR, China · Carnegie Mellon University, Pittsburgh PA 15213, USA · Monash University, Clayton VIC 3800, Australia
CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation
Apr 5, 2026Xiaojing Duan, Frederick Nwanganga, Chaoli WangMultiple-Choice Question Generation Generative AI in Education
University of Notre Dame, Notre Dame IN 46556, USA
Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports
Apr 4, 2026Yi-Cheng Wang, Wei-An Wang, Chu-Song ChenNumerical Reasoning in Language Models Financial QA
Department of Computer Science and Information Engineering, National Taiwan University · FinTech Center, National Taiwan University
Agile Story-Point Estimation: Is RAG a Better Way to Go?
Apr 3, 2026Lamyea Maha, Tajmilur Rahman, Chanchal RoySoftware Engineering Automated Software Engineering
University of Saskatchewan, Saskatoon, SK, Canada · Gannon University, Erie, PA, USA
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
Apr 3, 2026Prakhar Bansal, Shivangi AgarwalKnowledge Augmentation for Language Models Retrieval-Augmented Generation
Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework
Apr 3, 2026Kenichirou Narita, Siqi Peng, Taku Fukui +3Retrieval-Augmented Generation RAG Evaluation
Artificial Intelligence Laboratories, Fujitsu Limited
Principled and Scalable Diversity-Aware Retrieval via Cardinality-Constrained Binary Quadratic Programming
Apr 2, 2026Qiheng Lu, Nicholas D. SidiropoulosFrank-Wolfe Algorithms Constrained Optimization
University of Virginia Charlottesville, VA 22904, USA
Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning
Apr 2, 2026Saiful Islam Sagor, Tania Haghighi, Minhaj Nur Alam +1Domain Adaptation for LLMs LLM Fine-Tuning
Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, NC 28223, USA. · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA.
Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models
Apr 2, 2026Jae O Lee, Jaemin Kim, Sumyeong Ahn +1Retrieval-Augmented Generation Fine-Tuning
1Hanyang University · 2KENTECH
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Apr 2, 2026Yuhang Wu, Xiangqing Shen, Fanfan Wang +4Retrieval-Augmented Generation Reinforcement Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, China · School of Intelligence Science and Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models
Apr 2, 2026Florian Kelber, Matthias Jobst, Yuni Susanti +1AI-Assisted Scientific Research Retrieval-Augmented Generation
TU Dresden, Germany · FIZ Karlsruhe, Germany · ScaDS.AI, TU Dresden, Germany
PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation
Apr 2, 2026Yanxin Luo, Xiaoyu Zhang, Jing Li +2Emotional Support Conversation Emotion-Aware Dialogue Generation
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China · Oracle Cloud Infrastructure, Oracle, Santa Clara, CA, United States
Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
Apr 1, 2026Sha Li, Naren RamakrishnanRetrieval-Augmented Generation Multi-Agent Orchestration
Virginia Tech
Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
Apr 1, 2026Dumitru Verşebeniuc, Martijn Elands, Sara Falahatkar +4Educational QA Question Answering
Department of Advanced Computing Sciences, Maastricht University, Maastricht 6200 MD, The Netherlands
To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval
Apr 1, 2026Karan Singh, Michael Yu, Varun Gangal +4Retrieval-Augmented Generation Knowledge Augmentation for Language Models
Stanford University · †DegenAI Labs · Independent Researcher +3
The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products
Mar 31, 2026Alicia Bao, Jiamian He, Angel Hsu +3LLM Inference Efficiency LLM Inference
James · Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516 · Arboretica, Rotterdam, The Netherlands +2
PAR $^2$ -RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering
Mar 30, 2026Xingyu Li, Rongguang Wang, Yuying Wang +5Retrieval-Augmented Reasoning Multi-Document QA
Oracle AI
Courtroom-Style Multi-Agent Debate with Progressive RAG and Role-Switching for Controversial Claim Verification
Mar 30, 2026Masnun Nuha Chowdhury, Nusrat Jahan Beg, Umme Hunny Khan +3Claim Verification Multi-Agent Debate
Systems and Software Lab (SSL), Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh
Entropic Claim Resolution: Uncertainty-Driven Evidence Selection for RAG
Mar 30, 2026Davide Di GioiaRetrieval-Augmented Generation Evidence Selection
QCFuse: Query-Centric Cache Fusion for Efficient RAG Inference
Mar 30, 2026Jianxin Yan, Zeheng Qian, Wangze Ni +6LLM Inference Acceleration KV-Cache Management
Zhejiang University, Hangzhou, China · The University of Sydney, Sydney, Australia · Ant Group, Shanghai, Cina +4
ESGLens: An LLM-Based RAG Framework for Interactive ESG Report Analysis and Score Prediction
Mar 29, 2026Tsung-Yu Yang, Meng-Chi ChenDocument Information Extraction Evidence-Grounded Generation
Massachusetts Institute of Technology
Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
Mar 28, 2026Zequn Xie, Zhengyang SunHallucination in RAG LLM Hallucination Mitigation
Polytechnic Institute, Zhejiang University, Hangzhou, China · School of Computer Science and Technology, Xinjiang University, Urumqi, China
Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering
Mar 27, 2026Yoseph Berhanu Alebachew, Hunter Leary, Swanand Vaishampayan +1LLM Evaluation Software Engineering Benchmarks
Department of Computer Science, Virginia Tech Blacksburg, Virginia, USA
RAGnaroX: A Secure, Local-Hosted ChatOps Assistant Using Small Language Models
Mar 27, 2026Benedikt Dornauer, Mircea-Cristian RacasanRetrieval-Augmented Generation Tool Use
University of Innsbruck Innsbruck, Austria · c.c.com Moser GmbH Teslastraße 4, 8074 Grambach, Austria

Apr 6, 2026J/K move · Enter open · S save

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

Ziyi Chen, Mengxian Lyu, Cheng Peng, Yonghui Wu

Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL

Medical LLMs Clinical Language Model Evaluation Retrieval-Augmented Generation Long-Context Language Model Evaluation

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Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening. This study systematically explored both encoder- and decoder-based generative LLMs for screening clinical narratives to facilitate clinical trial recruitment. We examined both general-purpose LLMs and medical-adapted LLMs and explored three strategies to alleviate the "Lost in the Middle" issue when handling long documents, including 1) Original long-context: using the default context windows of LLMs, 2) NER-based extractive summarization: converting the long document into summarizations using named entity recognition, 3) RAG: dynamic evidence retrieval based on eligibility criteria. The 2018 N2C2 Track 1 benchmark dataset is used for evaluation. Our experimental results show that the MedGemma model with the RAG strategy achieved the best micro-F1 score of 89.05%, outperforming other models. Generative LLMs have remarkably improved trial criteria that require long-term reasoning across long documents, whereas trial criteria that span a short piece of context (e.g., lab tests) show incremental improvements. The real-world adoption of LLMs for trial recruitment must consider specific criteria for selecting among rule-based queries, encoder-based LLMs, and generative LLMs to maximize efficiency within reasonable computing costs.