{"total":215,"rows":[{"id":"2305.06983","title":"Active Retrieval Augmented Generation","created":"2023-05-11","cited_by_count":516,"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":"2305.15294","title":"Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy","created":"2023-05-24","cited_by_count":346,"area":"rag"},{"id":"2212.14024","title":"Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP","created":"2022-12-28","cited_by_count":318,"area":"rag"},{"id":"2405.14831","title":"HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models","created":"2024-05-23","cited_by_count":281,"area":"rag"},{"id":"2402.07630","title":"G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering","created":"2024-02-12","cited_by_count":223,"area":"rag"},{"id":"2305.14283","title":"Query Rewriting for Retrieval-Augmented Large Language Models","created":"2023-05-23","cited_by_count":213,"area":"rag"},{"id":"2311.09210","title":"Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models","created":"2023-11-15","cited_by_count":184,"area":"rag"},{"id":"2501.07572","title":"WebWalker: Benchmarking LLMs in Web Traversal","created":"2025-01-13","cited_by_count":175,"area":"rag"},{"id":"2312.07559","title":"PaperQA: Retrieval-Augmented Generative Agent for Scientific Research","created":"2023-12-08","cited_by_count":149,"area":"rag"},{"id":"2404.00610","title":"RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation","created":"2024-03-31","cited_by_count":147,"area":"rag"},{"id":"2405.07437","title":"Evaluation of Retrieval-Augmented Generation: A Survey","created":"2024-05-13","cited_by_count":146,"area":"rag"},{"id":"2303.01229","title":"Almanac: Retrieval-Augmented Language Models for Clinical Medicine","created":"2023-03-01","cited_by_count":123,"area":"rag"},{"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":"2407.11005","title":"RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems","created":"2024-06-25","cited_by_count":117,"area":"rag"},{"id":"2306.09224","title":"Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories","created":"2023-06-15","cited_by_count":107,"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":"2401.10225","title":"ChatQA: Surpassing GPT-4 on Conversational QA and RAG","created":"2024-01-18","cited_by_count":90,"area":"rag"},{"id":"2410.03727","title":"FaithEval: Can Your Language Model Stay Faithful to Context, Even If \"The Moon is Made of Marshmallows\"","created":"2024-09-30","cited_by_count":86,"area":"rag"},{"id":"2409.10102","title":"Trustworthiness in Retrieval-Augmented Generation Systems: A Survey","created":"2024-09-16","cited_by_count":83,"area":"rag"},{"id":"2409.14924","title":"Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely","created":"2024-09-23","cited_by_count":66,"area":"rag"},{"id":"2410.04343","title":"Inference Scaling for Long-Context Retrieval Augmented Generation","created":"2024-10-06","cited_by_count":65,"area":"rag"},{"id":"2405.12035","title":"KG-RAG: Bridging the Gap Between Knowledge and Creativity","created":"2024-05-20","cited_by_count":56,"area":"rag"},{"id":"2408.00727","title":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions","created":"2024-08-01","cited_by_count":54,"area":"rag"},{"id":"2310.07713","title":"InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining","created":"2023-10-11","cited_by_count":53,"area":"rag"},{"id":"2401.17043","title":"CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models","created":"2024-01-30","cited_by_count":50,"area":"rag"},{"id":"2411.19443","title":"Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models","created":"2024-11-29","cited_by_count":43,"area":"rag"},{"id":"2406.15187","title":"UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis","created":"2024-06-21","cited_by_count":40,"area":"rag"},{"id":"2406.19215","title":"SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation","created":"2024-06-27","cited_by_count":40,"area":"rag"},{"id":"2210.13693","title":"XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing","created":"2022-10-25","cited_by_count":33,"area":"rag"},{"id":"2402.11129","title":"BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering","created":"2024-02-16","cited_by_count":29,"area":"rag"},{"id":"2403.17359","title":"Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models","created":"2024-03-26","cited_by_count":27,"area":"rag"},{"id":"2406.18676","title":"Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation","created":"2024-06-26","cited_by_count":27,"area":"rag"},{"id":"2402.11626","title":"Metacognitive Retrieval-Augmented Large Language Models","created":"2024-02-18","cited_by_count":26,"area":"rag"},{"id":"2501.14050","title":"GraphRAG under Fire","created":"2025-01-23","cited_by_count":24,"area":"rag"},{"id":"2406.00456","title":"Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation","created":"2024-06-01","cited_by_count":23,"area":"rag"},{"id":"2403.19216","title":"Are Large Language Models Good at Utility Judgments?","created":"2024-03-28","cited_by_count":22,"area":"rag"},{"id":"2410.07071","title":"Retrieval-Augmented Decision Transformer: External Memory for In-context RL","created":"2024-10-09","cited_by_count":21,"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":"2410.18141","title":"SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback","created":"2024-10-22","cited_by_count":19,"area":"rag"},{"id":"2410.22954","title":"Retrieval-Augmented Generation with Estimation of Source Reliability","created":"2024-10-30","cited_by_count":19,"area":"rag"},{"id":"2402.07179","title":"Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models","created":"2024-02-11","cited_by_count":18,"area":"rag"},{"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":"2503.16528","title":"HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL","created":"2025-03-18","cited_by_count":17,"area":"rag"},{"id":"2410.01066","title":"From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems","created":"2024-10-01","cited_by_count":16,"area":"rag"},{"id":"2505.17762","title":"Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs","created":"2025-05-23","cited_by_count":16,"area":"rag"},{"id":"2305.17116","title":"Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model","created":"2023-05-26","cited_by_count":15,"area":"rag"},{"id":"2410.02721","title":"Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization","created":"2024-10-03","cited_by_count":15,"area":"rag"},{"id":"2605.14473","title":"Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict","created":"2026-05-14","cited_by_count":15,"area":"rag"},{"id":"2310.05388","title":"GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence","created":"2023-10-09","cited_by_count":14,"area":"rag"},{"id":"2502.11228","title":"Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs","created":"2025-02-16","cited_by_count":14,"area":"rag"},{"id":"2504.08893","title":"Knowledge Graph-extended Retrieval Augmented Generation for Question Answering","created":"2025-04-11","cited_by_count":14,"area":"rag"},{"id":"2403.14374","title":"FIT-RAG: Black-Box RAG with Factual Information and Token Reduction","created":"2024-03-21","cited_by_count":13,"area":"rag"},{"id":"2406.07053","title":"TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs","created":"2024-06-11","cited_by_count":13,"area":"rag"},{"id":"2307.05074","title":"Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain","created":"2023-07-11","cited_by_count":12,"area":"rag"},{"id":"2404.12560","title":"Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL","created":"2024-04-19","cited_by_count":12,"area":"rag"},{"id":"2410.11001","title":"Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs","created":"2024-10-14","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":"2408.01869","title":"MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance","created":"2024-08-03","cited_by_count":11,"area":"rag"},{"id":"2504.08231","title":"Out of Style: RAG's Fragility to Linguistic Variation","created":"2025-04-11","cited_by_count":11,"area":"rag"},{"id":"2506.00232","title":"ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering","created":"2025-05-30","cited_by_count":11,"area":"rag"},{"id":"2407.01080","title":"Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese","created":"2024-07-01","cited_by_count":10,"area":"rag"},{"id":"2507.02974","title":"InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy","created":"2025-06-30","cited_by_count":10,"area":"rag"},{"id":"2504.01018","title":"Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization","created":"2025-04-01","cited_by_count":9,"area":"rag"},{"id":"2504.13032","title":"InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning","created":"2025-04-17","cited_by_count":8,"area":"rag"},{"id":"2507.01297","title":"Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks","created":"2025-07-02","cited_by_count":8,"area":"rag"},{"id":"2508.14066","title":"Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation","created":"2025-08-11","cited_by_count":8,"area":"rag"},{"id":"2510.15261","title":"AUGUSTUS: An LLM-Driven Multimodal Agent System with Contextualized User Memory","created":"2025-10-17","cited_by_count":8,"area":"rag"},{"id":"2401.01511","title":"Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise","created":"2024-01-03","cited_by_count":7,"area":"rag"},{"id":"2410.03780","title":"Reward-RAG: Enhancing RAG with Reward Driven Supervision","created":"2024-10-03","cited_by_count":7,"area":"rag"},{"id":"2505.00972","title":"Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models","created":"2025-05-02","cited_by_count":7,"area":"rag"},{"id":"2508.03793","title":"AttnTrace: Contextual Attribution of Prompt Injection and Knowledge Corruption","created":"2025-08-05","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":"2405.05444","title":"Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large","created":"2024-05-08","cited_by_count":6,"area":"rag"},{"id":"2407.21055","title":"Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications","created":"2024-07-24","cited_by_count":6,"area":"rag"},{"id":"2408.03562","title":"A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case","created":"2024-08-07","cited_by_count":6,"area":"rag"},{"id":"2408.09017","title":"Meta Knowledge for Retrieval Augmented Large Language Models","created":"2024-08-16","cited_by_count":6,"area":"rag"},{"id":"2410.17406","title":"ProveRAG: Provenance-Driven Vulnerability Analysis with Automated Retrieval-Augmented LLMs","created":"2024-10-22","cited_by_count":6,"area":"rag"},{"id":"2412.08593","title":"Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks","created":"2024-12-11","cited_by_count":6,"area":"rag"},{"id":"2503.15454","title":"Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems","created":"2025-03-19","cited_by_count":6,"area":"rag"},{"id":"2505.02164","title":"Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use","created":"2025-05-04","cited_by_count":6,"area":"rag"},{"id":"2505.17238","title":"Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)","created":"2025-05-22","cited_by_count":6,"area":"rag"},{"id":"2509.21391","title":"MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering","created":"2025-09-24","cited_by_count":6,"area":"rag"},{"id":"2601.21162","title":"A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning","created":"2026-01-29","cited_by_count":6,"area":"rag"},{"id":"2403.19889","title":"Towards a Robust Retrieval-Based Summarization System","created":"2024-03-29","cited_by_count":5,"area":"rag"},{"id":"2406.12881","title":"Towards Unlocking Insights from Logbooks Using AI","created":"2024-05-25","cited_by_count":5,"area":"rag"},{"id":"2407.12468","title":"Evaluating Search Engines and Large Language Models for Answering Health Questions","created":"2024-07-17","cited_by_count":5,"area":"rag"},{"id":"2408.11043","title":"Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research","created":"2024-08-20","cited_by_count":5,"area":"rag"},{"id":"2410.13293","title":"SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation","created":"2024-10-17","cited_by_count":5,"area":"rag"},{"id":"2410.14881","title":"Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation","created":"2024-10-18","cited_by_count":5,"area":"rag"},{"id":"2504.01281","title":"Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding","created":"2025-04-02","cited_by_count":5,"area":"rag"},{"id":"2505.11271","title":"Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models","created":"2025-05-16","cited_by_count":5,"area":"rag"},{"id":"2505.16014","title":"Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains","created":"2025-05-21","cited_by_count":5,"area":"rag"},{"id":"2505.19538","title":"DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients","created":"2025-05-26","cited_by_count":5,"area":"rag"},{"id":"2505.20266","title":"syftr: Pareto-Optimal Generative AI","created":"2025-05-26","cited_by_count":5,"area":"rag"},{"id":"2506.17951","title":"A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment","created":"2025-06-22","cited_by_count":5,"area":"rag"},{"id":"2510.01375","title":"Fine-tuning with RAG for Improving LLM Learning of New Skills","created":"2025-10-01","cited_by_count":5,"area":"rag"},{"id":"2602.03689","title":"Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation","created":"2026-02-03","cited_by_count":5,"area":"rag"},{"id":"2603.24579","title":"MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination","created":"2026-03-25","cited_by_count":5,"area":"rag"}],"next_cursor":"100"}