{"total":127,"rows":[{"id":"2312.10997","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","created":"2023-12-18","cited_by_count":2212,"area":"lr"},{"id":"2405.06211","title":"A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models","created":"2024-05-10","cited_by_count":452,"area":"rag"},{"id":"2402.19473","title":"Retrieval-Augmented Generation for AI-Generated Content: A Survey","created":"2024-02-29","cited_by_count":354,"area":"rag"},{"id":"2405.20362","title":"Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools","created":"2024-05-30","cited_by_count":176,"area":null},{"id":"2401.01301","title":"Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models","created":"2024-01-02","cited_by_count":150,"area":null},{"id":"2309.14402","title":"Physics of Language Models: Part 3.2, Knowledge Manipulation","created":"2023-09-25","cited_by_count":143,"area":null},{"id":"2401.05856","title":"Seven Failure Points When Engineering a Retrieval Augmented Generation System","created":"2024-01-11","cited_by_count":139,"area":"rag"},{"id":"2407.11005","title":"RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems","created":"2024-06-25","cited_by_count":117,"area":"rag"},{"id":"2406.13629","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","created":"2024-06-19","cited_by_count":101,"area":"rag"},{"id":"2404.12457","title":"RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation","created":"2024-04-18","cited_by_count":98,"area":"rag"},{"id":"2406.00083","title":"BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models","created":"2024-06-03","cited_by_count":86,"area":"rag"},{"id":"2404.07220","title":"Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers","created":"2024-03-22","cited_by_count":84,"area":"rag"},{"id":"2403.05313","title":"RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation","created":"2024-03-08","cited_by_count":75,"area":"lr"},{"id":"2410.07590","title":"TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text","created":"2024-10-10","cited_by_count":63,"area":"rag"},{"id":"2405.18414","title":"Don't Forget to Connect! Improving RAG with Graph-based Reranking","created":"2024-05-28","cited_by_count":49,"area":"rag"},{"id":"2403.08946","title":"Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era","created":"2024-03-13","cited_by_count":46,"area":null},{"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":"2407.13101","title":"Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach","created":"2024-07-18","cited_by_count":40,"area":"rag"},{"id":"2310.18344","title":"Chainpoll: A high efficacy method for LLM hallucination detection","created":"2023-10-22","cited_by_count":39,"area":null},{"id":"2312.06185","title":"KnowGPT: Knowledge Graph based Prompting for Large Language Models","created":"2023-12-11","cited_by_count":31,"area":null},{"id":"2507.18910","title":"A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions","created":"2025-07-25","cited_by_count":31,"area":"rag"},{"id":"2407.09394","title":"PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents","created":"2024-07-12","cited_by_count":30,"area":"rag"},{"id":"2509.21212","title":"SGMem: Sentence Graph Memory for Long-Term Conversational Agents","created":"2025-09-25","cited_by_count":29,"area":"agents"},{"id":"2504.10147","title":"A Survey of Personalization: From RAG to Agent","created":"2025-04-14","cited_by_count":27,"area":"rag"},{"id":"2404.15939","title":"Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications","created":"2024-04-24","cited_by_count":26,"area":"rag"},{"id":"2311.10776","title":"Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis","created":"2023-11-16","cited_by_count":25,"area":"agents"},{"id":"2410.15267","title":"When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?","created":"2024-10-20","cited_by_count":24,"area":"rag"},{"id":"2309.16035","title":"MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering","created":"2023-09-27","cited_by_count":23,"area":"rag"},{"id":"2405.00175","title":"Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models","created":"2024-04-30","cited_by_count":23,"area":"rag"},{"id":"2408.04870","title":"ConfusedPilot: Confused Deputy Risks in RAG-based LLMs","created":"2024-08-09","cited_by_count":22,"area":"rag"},{"id":"2409.09510","title":"Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models","created":"2024-09-14","cited_by_count":21,"area":null},{"id":"2505.07671","title":"Benchmarking Retrieval-Augmented Generation for Chemistry","created":"2025-05-12","cited_by_count":20,"area":"rag"},{"id":"2502.19781","title":"RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings","created":"2025-02-27","cited_by_count":19,"area":null},{"id":"2504.01346","title":"RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking","created":"2025-04-02","cited_by_count":18,"area":"rag"},{"id":"2504.09602","title":"Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations","created":"2025-04-13","cited_by_count":18,"area":null},{"id":"2307.03027","title":"Improving Retrieval-Augmented Large Language Models via Data Importance Learning","created":"2023-07-06","cited_by_count":17,"area":"rag"},{"id":"2406.12824","title":"From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries","created":"2024-06-18","cited_by_count":16,"area":"rag"},{"id":"2506.06151","title":"Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems","created":"2025-06-06","cited_by_count":16,"area":"rag"},{"id":"2512.03413","title":"BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents","created":"2025-12-03","cited_by_count":16,"area":"rag"},{"id":"2410.09942","title":"Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization","created":"2024-10-13","cited_by_count":14,"area":"rag"},{"id":"2412.15246","title":"Accelerating Retrieval-Augmented Generation","created":"2024-12-14","cited_by_count":14,"area":"rag"},{"id":"2409.13265","title":"Towards LifeSpan Cognitive Systems","created":"2024-09-20","cited_by_count":13,"area":null},{"id":"2502.18470","title":"Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions","created":"2025-02-04","cited_by_count":13,"area":"lr"},{"id":"2603.26815","title":"Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off","created":"2026-03-26","cited_by_count":13,"area":"rag"},{"id":"2407.12216","title":"Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation","created":"2024-07-16","cited_by_count":12,"area":"rag"},{"id":"2412.07548","title":"Automatic Database Configuration Debugging using Retrieval-Augmented Language Models","created":"2024-12-10","cited_by_count":12,"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":"2508.20131","title":"ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation","created":"2025-08-26","cited_by_count":12,"area":"rag"},{"id":"2312.07592","title":"Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Models","created":"2023-12-11","cited_by_count":11,"area":null},{"id":"2302.11352","title":"X-TRA: Improving Chest X-ray Tasks with Cross-Modal Retrieval Augmentation","created":"2023-02-22","cited_by_count":10,"area":null},{"id":"2406.10261","title":"FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination","created":"2024-06-11","cited_by_count":10,"area":null},{"id":"2502.10648","title":"LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization","created":"2025-02-15","cited_by_count":10,"area":null},{"id":"2509.04499","title":"DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence","created":"2025-09-02","cited_by_count":10,"area":"agents"},{"id":"2402.17081","title":"A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge","created":"2024-02-26","cited_by_count":9,"area":"rag"},{"id":"2410.07589","title":"No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users","created":"2024-10-10","cited_by_count":9,"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":"2507.13396","title":"DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning","created":"2025-07-16","cited_by_count":8,"area":"lr"},{"id":"2410.03780","title":"Reward-RAG: Enhancing RAG with Reward Driven Supervision","created":"2024-10-03","cited_by_count":7,"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":"2512.08892","title":"Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders","created":"2025-12-09","cited_by_count":7,"area":"rag"},{"id":"2501.05249","title":"RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models","created":"2025-01-09","cited_by_count":6,"area":"rag"},{"id":"2506.04565","title":"From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2509.03787","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","created":"2025-09-04","cited_by_count":6,"area":"rag"},{"id":"2212.10381","title":"To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering","created":"2022-12-20","cited_by_count":5,"area":null},{"id":"2404.09022","title":"Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies","created":"2024-04-13","cited_by_count":5,"area":null},{"id":"2408.08624","title":"RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions","created":"2024-08-16","cited_by_count":5,"area":null},{"id":"2406.05187","title":"How to Strategize Human Content Creation in the Era of GenAI?","created":"2024-06-07","cited_by_count":4,"area":null},{"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":"2504.16813","title":"LLM-assisted Graph-RAG Information Extraction from IFC Data","created":"2025-04-23","cited_by_count":4,"area":null},{"id":"2504.17421","title":"Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2505.17058","title":"DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation","created":"2025-05-17","cited_by_count":4,"area":"rag"},{"id":"2406.17807","title":"Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary","created":"2024-06-23","cited_by_count":3,"area":null},{"id":"2410.14740","title":"Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2502.15690","title":"Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents","created":"2024-12-20","cited_by_count":3,"area":"agents"},{"id":"2504.11765","title":"Shared Disk KV Cache Management for Efficient Multi-Instance Inference in RAG-Powered LLMs","created":"2025-04-16","cited_by_count":3,"area":"inference"},{"id":"2505.21907","title":"Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy","created":"2025-05-28","cited_by_count":3,"area":"alignment"},{"id":"2507.08862","title":"RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation","created":"2025-07-09","cited_by_count":3,"area":"rag"},{"id":"2511.08505","title":"Structured RAG for Answering Aggregative Questions","created":"2025-11-11","cited_by_count":3,"area":"rag"},{"id":"2402.12177","title":"Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning","created":"2024-02-19","cited_by_count":2,"area":"rag"},{"id":"2411.15203","title":"Multimodal large language model for wheat breeding: a new exploration of smart breeding","created":"2024-11-20","cited_by_count":2,"area":"mllm"},{"id":"2502.10976","title":"QuOTE: Question-Oriented Text Embeddings","created":"2025-02-16","cited_by_count":2,"area":"rag"},{"id":"2503.16581","title":"Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models","created":"2025-03-20","cited_by_count":2,"area":"rag"},{"id":"2504.04065","title":"Enabling Collaborative Parametric Knowledge Calibration for Retrieval-Augmented Vision Question Answering","created":"2025-04-05","cited_by_count":2,"area":"rag"},{"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":"2512.14792","title":"IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection","created":"2025-12-16","cited_by_count":2,"area":"rag"},{"id":"2605.03344","title":"RAG over Thinking Traces Can Improve Reasoning Tasks","created":"2026-05-05","cited_by_count":2,"area":"rag"},{"id":"2403.10559","title":"Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI","created":"2024-03-14","cited_by_count":1,"area":null},{"id":"2405.06003","title":"Binary Hypothesis Testing for Softmax Models and Leverage Score Models","created":"2024-05-09","cited_by_count":1,"area":null},{"id":"2407.12749","title":"ChipXplore: Natural Language Exploration of Hardware Designs and Libraries","created":"2024-07-17","cited_by_count":1,"area":null},{"id":"2501.17397","title":"Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains","created":"2025-01-29","cited_by_count":1,"area":"rag"},{"id":"2501.17802","title":"LEKA:LLM-Enhanced Knowledge Augmentation","created":"2025-01-29","cited_by_count":1,"area":null},{"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":"2504.07754","title":"Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation","created":"2025-04-10","cited_by_count":1,"area":null},{"id":"2505.00989","title":"VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language","created":"2025-05-02","cited_by_count":1,"area":"agents"},{"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":"2506.07037","title":"KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering","created":"2025-06-08","cited_by_count":1,"area":"rag"},{"id":"2508.18168","title":"Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation","created":"2025-08-25","cited_by_count":1,"area":"rag"},{"id":"2508.18929","title":"Diverse And Private Synthetic Datasets Generation for RAG evaluation: A multi-agent framework","created":"2025-08-26","cited_by_count":1,"area":"rag"},{"id":"2508.19357","title":"Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains","created":"2025-08-26","cited_by_count":1,"area":"rag"},{"id":"2509.05716","title":"A Survey of the State-of-the-Art in Conversational Question Answering Systems","created":"2025-09-06","cited_by_count":1,"area":null}],"next_cursor":"100"}