{"total":146,"rows":[{"id":"2408.08921","title":"Graph Retrieval-Augmented Generation: A Survey","created":"2024-08-15","cited_by_count":324,"area":"rag"},{"id":"2409.12941","title":"Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation","created":"2024-09-19","cited_by_count":162,"area":"lr"},{"id":"2410.05983","title":"Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG","created":"2024-10-08","cited_by_count":123,"area":"rag"},{"id":"2408.10343","title":"LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain","created":"2024-08-19","cited_by_count":118,"area":"rag"},{"id":"2408.08067","title":"RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation","created":"2024-08-15","cited_by_count":61,"area":"rag"},{"id":"2502.11371","title":"RAG vs. GraphRAG: A Systematic Evaluation and Key Insights","created":"2025-02-17","cited_by_count":53,"area":"rag"},{"id":"2503.04338","title":"In-depth Analysis of Graph-based RAG in a Unified Framework","created":"2025-03-06","cited_by_count":46,"area":"rag"},{"id":"2503.10677","title":"A Survey on Knowledge-Oriented Retrieval-Augmented Generation","created":"2025-03-11","cited_by_count":36,"area":"rag"},{"id":"2503.18016","title":"Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook","created":"2025-03-23","cited_by_count":32,"area":"rag"},{"id":"2505.20099","title":"Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities","created":"2025-05-26","cited_by_count":31,"area":null},{"id":"2502.09304","title":"KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG","created":"2025-02-13","cited_by_count":29,"area":"rag"},{"id":"2502.09891","title":"ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation","created":"2025-02-14","cited_by_count":28,"area":"rag"},{"id":"2504.08758","title":"Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation","created":"2025-03-30","cited_by_count":23,"area":"rag"},{"id":"2407.21712","title":"Adaptive Retrieval-Augmented Generation for Conversational Systems","created":"2024-07-31","cited_by_count":22,"area":"rag"},{"id":"2504.14891","title":"Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey","created":"2025-04-21","cited_by_count":20,"area":"rag"},{"id":"2504.18243","title":"DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering","created":"2025-04-25","cited_by_count":20,"area":null},{"id":"2406.05085","title":"Multi-Head RAG: Solving Multi-Aspect Problems with LLMs","created":"2024-06-07","cited_by_count":18,"area":"rag"},{"id":"2504.10499","title":"Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey","created":"2025-04-08","cited_by_count":18,"area":"rag"},{"id":"2406.11424","title":"Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability","created":"2024-06-17","cited_by_count":15,"area":"rag"},{"id":"2502.01523","title":"CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering","created":"2025-02-03","cited_by_count":15,"area":null},{"id":"2510.07728","title":"Who Stole Your Data? A Method for Detecting Unauthorized RAG Theft","created":"2025-10-09","cited_by_count":15,"area":"rag"},{"id":"2504.15205","title":"Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges","created":"2025-04-21","cited_by_count":14,"area":"rag"},{"id":"2502.15954","title":"MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning","created":"2025-02-21","cited_by_count":13,"area":"rag"},{"id":"2504.16787","title":"Credible Plan-Driven RAG Method for Multi-Hop Question Answering","created":"2025-04-23","cited_by_count":12,"area":"rag"},{"id":"2503.13299","title":"A Survey on Transformer Context Extension: Approaches and Evaluation","created":"2025-03-17","cited_by_count":11,"area":null},{"id":"2510.11423","title":"Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation","created":"2025-10-13","cited_by_count":10,"area":null},{"id":"2510.13975","title":"Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems","created":"2025-10-15","cited_by_count":10,"area":"rag"},{"id":"2410.15531","title":"Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage","created":"2024-10-20","cited_by_count":8,"area":"rag"},{"id":"2501.15067","title":"CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs","created":"2025-01-25","cited_by_count":8,"area":"rag"},{"id":"2506.20978","title":"Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality","created":"2025-06-26","cited_by_count":8,"area":"rag"},{"id":"2410.12812","title":"Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective","created":"2024-10-01","cited_by_count":7,"area":"rag"},{"id":"2411.19463","title":"Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems","created":"2024-11-29","cited_by_count":7,"area":"rag"},{"id":"2504.20119","title":"Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets","created":"2025-04-28","cited_by_count":7,"area":"rag"},{"id":"2601.14952","title":"CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning","created":"2026-01-21","cited_by_count":7,"area":"lr"},{"id":"2410.08801","title":"A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation","created":"2024-10-11","cited_by_count":6,"area":"rag"},{"id":"2503.09956","title":"DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey","created":"2025-03-13","cited_by_count":6,"area":null},{"id":"2505.21785","title":"Born a Transformer -- Always a Transformer? On the Effect of Pretraining on Architectural Abilities","created":"2025-05-27","cited_by_count":6,"area":null},{"id":"2603.04772","title":"TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings","created":"2026-03-05","cited_by_count":6,"area":"lr"},{"id":"2408.02545","title":"RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation","created":"2024-08-05","cited_by_count":5,"area":"rag"},{"id":"2409.06595","title":"GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering","created":"2024-09-10","cited_by_count":5,"area":"rag"},{"id":"2410.12857","title":"Enterprise Benchmarks for Large Language Model Evaluation","created":"2024-10-11","cited_by_count":5,"area":null},{"id":"2504.05324","title":"Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis","created":"2025-02-28","cited_by_count":5,"area":null},{"id":"2505.12065","title":"Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents","created":"2025-05-17","cited_by_count":5,"area":"agents"},{"id":"2507.05713","title":"DRAGOn: Designing RAG On Periodically Updated Corpus","created":"2025-07-08","cited_by_count":5,"area":"rag"},{"id":"2508.03644","title":"Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?","created":"2025-08-05","cited_by_count":5,"area":"mllm"},{"id":"2408.00307","title":"ABC Align: Large Language Model Alignment for Safety & Accuracy","created":"2024-08-01","cited_by_count":4,"area":"alignment"},{"id":"2410.13671","title":"HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings","created":"2024-10-17","cited_by_count":4,"area":"rag"},{"id":"2411.19554","title":"Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs","created":"2024-11-29","cited_by_count":4,"area":null},{"id":"2412.12300","title":"Unanswerability Evaluation for Retrieval Augmented Generation","created":"2024-12-16","cited_by_count":4,"area":"rag"},{"id":"2501.08208","title":"ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems","created":"2025-01-14","cited_by_count":4,"area":"rag"},{"id":"2502.15854","title":"Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning Models","created":"2025-02-21","cited_by_count":4,"area":"lr"},{"id":"2506.16444","title":"REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing","created":"2025-06-19","cited_by_count":4,"area":null},{"id":"2507.08445","title":"Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval","created":"2025-07-11","cited_by_count":4,"area":"rag"},{"id":"2508.06056","title":"RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation","created":"2025-08-08","cited_by_count":4,"area":"rag"},{"id":"2602.15856","title":"Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspective","created":"2026-01-25","cited_by_count":4,"area":"lr"},{"id":"2406.14972","title":"A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems","created":"2024-06-21","cited_by_count":3,"area":"rag"},{"id":"2409.10011","title":"HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making","created":"2024-09-16","cited_by_count":3,"area":"rag"},{"id":"2409.15763","title":"IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios","created":"2024-09-24","cited_by_count":3,"area":"rag"},{"id":"2504.08756","title":"MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation","created":"2025-03-29","cited_by_count":3,"area":null},{"id":"2506.10467","title":"Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications","created":"2025-06-12","cited_by_count":3,"area":"lr"},{"id":"2509.05607","title":"CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines","created":"2025-09-06","cited_by_count":3,"area":null},{"id":"2511.00505","title":"Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge","created":"2025-11-01","cited_by_count":3,"area":"rag"},{"id":"2601.03542","title":"Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models","created":"2026-01-07","cited_by_count":3,"area":"lr"},{"id":"2601.07880","title":"Sola-Visibility-ISPM: Benchmarking Agentic AI for Identity Security Posture Management Visibility","created":"2026-01-11","cited_by_count":3,"area":"agents"},{"id":"2601.12499","title":"Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck","created":"2026-01-18","cited_by_count":3,"area":"lr"},{"id":"2410.09623","title":"Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation","created":"2024-10-12","cited_by_count":2,"area":"rag"},{"id":"2410.20299","title":"EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update","created":"2024-10-27","cited_by_count":2,"area":"rag"},{"id":"2502.19779","title":"Do Retrieval-Augmented Language Models Adapt to Varying User Needs?","created":"2025-02-27","cited_by_count":2,"area":"rag"},{"id":"2503.02922","title":"Optimizing open-domain question answering with graph-based retrieval augmented generation","created":"2025-03-04","cited_by_count":2,"area":"rag"},{"id":"2503.04789","title":"Aligning Extraction and Generation for Robust Retrieval-Augmented Generation","created":"2025-02-28","cited_by_count":2,"area":"rag"},{"id":"2504.07803","title":"A System for Comprehensive Assessment of RAG Frameworks","created":"2025-04-10","cited_by_count":2,"area":"rag"},{"id":"2505.08445","title":"Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency","created":"2025-05-13","cited_by_count":2,"area":"rag"},{"id":"2505.13994","title":"Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning","created":"2025-05-20","cited_by_count":2,"area":"rag"},{"id":"2505.14838","title":"In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2506.06331","title":"How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG","created":"2025-05-31","cited_by_count":2,"area":"rag"},{"id":"2506.12494","title":"FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation","created":"2025-06-14","cited_by_count":2,"area":"rag"},{"id":"2507.03608","title":"Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)","created":"2025-07-04","cited_by_count":2,"area":"rag"},{"id":"2507.21099","title":"Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting","created":"2025-07-03","cited_by_count":2,"area":null},{"id":"2508.11758","title":"Can we Evaluate RAGs with Synthetic Data?","created":"2025-08-15","cited_by_count":2,"area":"synth"},{"id":"2510.21459","title":"SBASH: a Framework for Designing and Evaluating RAG vs. Prompt-Tuned LLM Honeypots","created":"2025-10-24","cited_by_count":2,"area":null},{"id":"2511.16283","title":"MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering","created":"2025-11-20","cited_by_count":2,"area":"rag"},{"id":"2601.08747","title":"To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning","created":"2026-01-13","cited_by_count":2,"area":"lr"},{"id":"2601.12991","title":"RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems","created":"2026-01-19","cited_by_count":2,"area":"rag"},{"id":"2604.05467","title":"CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation","created":"2026-04-07","cited_by_count":2,"area":"rag"},{"id":"2604.08304","title":"Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions","created":"2026-04-09","cited_by_count":2,"area":"rag"},{"id":"2411.04473","title":"ML-Promise: A Multilingual Dataset for Corporate Promise Verification","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2411.11895","title":"Deploying Large Language Models With Retrieval Augmented Generation","created":"2024-11-07","cited_by_count":1,"area":"rag"},{"id":"2412.15443","title":"SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval","created":"2024-12-19","cited_by_count":1,"area":"rag"},{"id":"2501.08262","title":"Addressing the sustainable AI trilemma: a case study on LLM agents and RAG","created":"2025-01-14","cited_by_count":1,"area":"agents"},{"id":"2502.19596","title":"Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documents","created":"2025-02-26","cited_by_count":1,"area":"rag"},{"id":"2503.04153","title":"KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease","created":"2025-03-06","cited_by_count":1,"area":"lr"},{"id":"2505.10951","title":"SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache","created":"2025-05-16","cited_by_count":1,"area":"rag"},{"id":"2507.13827","title":"Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models","created":"2025-07-18","cited_by_count":1,"area":null},{"id":"2507.16826","title":"A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models","created":"2025-07-07","cited_by_count":1,"area":"rag"},{"id":"2508.19093","title":"Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index","created":"2025-08-26","cited_by_count":1,"area":"rag"},{"id":"2509.09651","title":"Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations","created":"2025-09-11","cited_by_count":1,"area":"rag"},{"id":"2509.21237","title":"Query-Centric Graph Retrieval Augmented Generation","created":"2025-09-25","cited_by_count":1,"area":"lr"},{"id":"2509.26205","title":"Human-Centered Evaluation of RAG outputs: a framework and questionnaire for human-AI collaboration","created":"2025-09-30","cited_by_count":1,"area":"rag"},{"id":"2510.00482","title":"Agent Fine-tuning through Distillation for Domain-specific LLMs in Microdomains","created":"2025-10-01","cited_by_count":1,"area":"agents"},{"id":"2510.20505","title":"RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA","created":"2025-10-23","cited_by_count":1,"area":"rag"}],"next_cursor":"100"}