{"total":409,"rows":[{"id":"2310.11511","title":"Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection","created":"2023-10-17","cited_by_count":1839,"area":"rag"},{"id":"2212.10511","title":"When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories","created":"2022-12-20","cited_by_count":1128,"area":"rag"},{"id":"2208.03299","title":"Atlas: Few-shot Learning with Retrieval Augmented Language Models","created":"2022-08-05","cited_by_count":952,"area":"rag"},{"id":"2301.12652","title":"REPLUG: Retrieval-Augmented Black-Box Language Models","created":"2023-01-30","cited_by_count":792,"area":"rag"},{"id":"2302.00083","title":"In-Context Retrieval-Augmented Language Models","created":"2023-01-31","cited_by_count":702,"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":"2402.13178","title":"Benchmarking Retrieval-Augmented Generation for Medicine","created":"2024-02-20","cited_by_count":331,"area":"rag"},{"id":"2401.14887","title":"The Power of Noise: Redefining Retrieval for RAG Systems","created":"2024-01-26","cited_by_count":281,"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":"2402.07867","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","created":"2024-02-12","cited_by_count":267,"area":"rag"},{"id":"2310.04408","title":"RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation","created":"2023-10-06","cited_by_count":256,"area":"rag"},{"id":"2502.14802","title":"From RAG to Memory: Non-Parametric Continual Learning for Large Language Models","created":"2025-02-20","cited_by_count":231,"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":"2407.02485","title":"RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs","created":"2024-07-02","cited_by_count":202,"area":"rag"},{"id":"2401.15884","title":"Corrective Retrieval Augmented Generation","created":"2024-01-29","cited_by_count":197,"area":"rag"},{"id":"2310.01352","title":"RA-DIT: Retrieval-Augmented Dual Instruction Tuning","created":"2023-10-02","cited_by_count":195,"area":"rag"},{"id":"2411.13093","title":"Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension","created":"2024-11-20","cited_by_count":138,"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":"2407.16833","title":"Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach","created":"2024-07-23","cited_by_count":119,"area":"rag"},{"id":"2406.05870","title":"Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents","created":"2024-06-09","cited_by_count":102,"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":"2404.05970","title":"Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation","created":"2024-04-09","cited_by_count":99,"area":"rag"},{"id":"2406.14497","title":"CodeRAG-Bench: Can Retrieval Augment Code Generation?","created":"2024-06-20","cited_by_count":91,"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":"2404.13781","title":"Evaluating Retrieval Quality in Retrieval-Augmented Generation","created":"2024-04-21","cited_by_count":90,"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":"2407.01219","title":"Searching for Best Practices in Retrieval-Augmented Generation","created":"2024-07-01","cited_by_count":83,"area":"rag"},{"id":"2502.04413","title":"MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot","created":"2025-02-06","cited_by_count":83,"area":"rag"},{"id":"2405.16506","title":"GRAG: Graph Retrieval-Augmented Generation","created":"2024-05-26","cited_by_count":78,"area":"rag"},{"id":"2404.10981","title":"A Survey on Retrieval-Augmented Text Generation for Large Language Models","created":"2024-04-17","cited_by_count":77,"area":"rag"},{"id":"2407.08223","title":"Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting","created":"2024-07-11","cited_by_count":77,"area":"rag"},{"id":"2403.03187","title":"Reliable, Adaptable, and Attributable Language Models with Retrieval","created":"2024-03-05","cited_by_count":73,"area":"rag"},{"id":"2407.13193","title":"Retrieval-Augmented Generation for Natural Language Processing: A Survey","created":"2024-07-18","cited_by_count":71,"area":"rag"},{"id":"2401.06954","title":"Bridging the Preference Gap between Retrievers and LLMs","created":"2024-01-13","cited_by_count":69,"area":"rag"},{"id":"2405.13401","title":"TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models","created":"2024-05-22","cited_by_count":67,"area":"rag"},{"id":"2409.13731","title":"KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation","created":"2024-09-10","cited_by_count":67,"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":"2404.13948","title":"Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations","created":"2024-04-22","cited_by_count":65,"area":"rag"},{"id":"2310.07554","title":"A Multi-Task Embedder For Retrieval Augmented LLMs","created":"2023-10-11","cited_by_count":64,"area":"rag"},{"id":"2407.09014","title":"CompAct: Compressing Retrieved Documents Actively for Question Answering","created":"2024-07-12","cited_by_count":64,"area":"rag"},{"id":"2304.06762","title":"Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study","created":"2023-04-13","cited_by_count":63,"area":"rag"},{"id":"2406.19417","title":"\"Glue pizza and eat rocks\" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models","created":"2024-06-26","cited_by_count":60,"area":"rag"},{"id":"2504.09795","title":"VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents","created":"2025-04-14","cited_by_count":59,"area":"rag"},{"id":"2501.05874","title":"VideoRAG: Retrieval-Augmented Generation over Video Corpus","created":"2025-01-10","cited_by_count":57,"area":"rag"},{"id":"2310.07713","title":"InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining","created":"2023-10-11","cited_by_count":53,"area":"rag"},{"id":"2403.01432","title":"Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge","created":"2024-03-03","cited_by_count":50,"area":"rag"},{"id":"2405.20485","title":"Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation","created":"2024-05-30","cited_by_count":49,"area":"rag"},{"id":"2403.06840","title":"RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback","created":"2024-03-11","cited_by_count":46,"area":"rag"},{"id":"2501.00332","title":"MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation","created":"2024-12-31","cited_by_count":44,"area":"rag"},{"id":"2502.08826","title":"Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation","created":"2025-02-12","cited_by_count":42,"area":"rag"},{"id":"2501.00879","title":"TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation","created":"2025-01-01","cited_by_count":40,"area":"rag"},{"id":"2502.01113","title":"GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation","created":"2025-02-03","cited_by_count":40,"area":"rag"},{"id":"2501.15228","title":"Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning","created":"2025-01-25","cited_by_count":39,"area":"rag"},{"id":"2310.18347","title":"PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter","created":"2023-10-23","cited_by_count":38,"area":"rag"},{"id":"2308.07922","title":"RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models","created":"2023-08-15","cited_by_count":37,"area":"rag"},{"id":"2504.08748","title":"A Survey of Multimodal Retrieval-Augmented Generation","created":"2025-03-26","cited_by_count":37,"area":"rag"},{"id":"2504.03957","title":"Practical Poisoning Attacks against Retrieval-Augmented Generation","created":"2025-04-04","cited_by_count":36,"area":"rag"},{"id":"2505.16470","title":"Benchmarking Retrieval-Augmented Multimodal Generation for Document Question Answering","created":"2025-05-22","cited_by_count":36,"area":"rag"},{"id":"2411.18948","title":"RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis","created":"2024-11-28","cited_by_count":33,"area":"rag"},{"id":"2403.14952","title":"Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation","created":"2024-03-22","cited_by_count":32,"area":"rag"},{"id":"2504.07717","title":"PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization","created":"2025-04-10","cited_by_count":32,"area":"rag"},{"id":"2407.01102","title":"BERGEN: A Benchmarking Library for Retrieval-Augmented Generation","created":"2024-07-01","cited_by_count":31,"area":"rag"},{"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":"2402.17497","title":"REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering","created":"2024-02-27","cited_by_count":28,"area":"rag"},{"id":"2504.21668","title":"Traceback of Poisoning Attacks to Retrieval-Augmented Generation","created":"2025-04-30","cited_by_count":28,"area":"rag"},{"id":"2505.11548","title":"One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems","created":"2025-05-15","cited_by_count":28,"area":"rag"},{"id":"2502.12586","title":"G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation","created":"2025-02-18","cited_by_count":27,"area":"rag"},{"id":"2312.15883","title":"HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses","created":"2023-12-26","cited_by_count":25,"area":"rag"},{"id":"2404.19543","title":"RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing","created":"2024-04-30","cited_by_count":25,"area":"rag"},{"id":"2405.16178","title":"Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection","created":"2024-05-25","cited_by_count":25,"area":"rag"},{"id":"2406.19234","title":"Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation","created":"2024-06-27","cited_by_count":25,"area":"rag"},{"id":"2501.02968","title":"FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models","created":"2025-01-06","cited_by_count":25,"area":"rag"},{"id":"2408.04259","title":"EfficientRAG: Efficient Retriever for Multi-Hop Question Answering","created":"2024-08-08","cited_by_count":24,"area":"rag"},{"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":"2503.01478","title":"SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction","created":"2025-03-03","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":"2504.18070","title":"PropRAG: Guiding Retrieval with Beam Search over Proposition Paths","created":"2025-04-25","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":"2410.14057","title":"Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs","created":"2024-10-17","cited_by_count":22,"area":"rag"},{"id":"2406.05087","title":"Corpus Poisoning via Approximate Greedy Gradient Descent","created":"2024-06-07","cited_by_count":21,"area":"rag"},{"id":"2410.15737","title":"Who's Who: Large Language Models Meet Knowledge Conflicts in Practice","created":"2024-10-21","cited_by_count":21,"area":"rag"},{"id":"2506.08479","title":"Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$","created":"2025-06-10","cited_by_count":21,"area":"rag"},{"id":"2306.13421","title":"Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval","created":"2023-06-23","cited_by_count":20,"area":"rag"},{"id":"2401.14021","title":"Accelerating Retrieval-Augmented Language Model Serving with Speculation","created":"2024-01-25","cited_by_count":20,"area":"rag"},{"id":"2411.14572","title":"Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective","created":"2024-11-21","cited_by_count":20,"area":"rag"},{"id":"2412.20953","title":"GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search","created":"2024-12-30","cited_by_count":20,"area":"rag"},{"id":"2505.07671","title":"Benchmarking Retrieval-Augmented Generation for Chemistry","created":"2025-05-12","cited_by_count":20,"area":"rag"},{"id":"2406.17519","title":"Entropy-Based Decoding for Retrieval-Augmented Large Language Models","created":"2024-06-25","cited_by_count":19,"area":"rag"},{"id":"2408.01084","title":"Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts","created":"2024-08-02","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":"2412.13163","title":"C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System","created":"2024-12-17","cited_by_count":19,"area":"rag"},{"id":"2510.25025","title":"Secure Retrieval-Augmented Generation against Poisoning Attacks","created":"2025-10-28","cited_by_count":19,"area":"rag"},{"id":"2406.13779","title":"FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering","created":"2024-06-19","cited_by_count":18,"area":"rag"},{"id":"2406.17186","title":"CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation","created":"2024-06-24","cited_by_count":18,"area":"rag"},{"id":"2410.10293","title":"FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG","created":"2024-10-14","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":"2505.18543","title":"Benchmarking Poisoning Attacks against Retrieval-Augmented Generation","created":"2025-05-24","cited_by_count":18,"area":"rag"},{"id":"2406.15264","title":"Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics","created":"2024-06-21","cited_by_count":17,"area":"rag"},{"id":"2412.12559","title":"EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation","created":"2024-12-17","cited_by_count":17,"area":"rag"}],"next_cursor":"100"}