{"total":236,"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":"2305.06983","title":"Active Retrieval Augmented Generation","created":"2023-05-11","cited_by_count":516,"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":"2408.08921","title":"Graph Retrieval-Augmented Generation: A Survey","created":"2024-08-15","cited_by_count":324,"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":"2401.00396","title":"RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models","created":"2023-12-31","cited_by_count":283,"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":"2501.07572","title":"WebWalker: Benchmarking LLMs in Web Traversal","created":"2025-01-13","cited_by_count":175,"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":"2507.03724","title":"MemOS: A Memory OS for AI System","created":"2025-07-04","cited_by_count":138,"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":"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":"2407.01219","title":"Searching for Best Practices in Retrieval-Augmented Generation","created":"2024-07-01","cited_by_count":83,"area":"rag"},{"id":"2401.08406","title":"RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture","created":"2024-01-16","cited_by_count":81,"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":"2403.03187","title":"Reliable, Adaptable, and Attributable Language Models with Retrieval","created":"2024-03-05","cited_by_count":73,"area":"rag"},{"id":"2310.04027","title":"Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models","created":"2023-10-06","cited_by_count":68,"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":"2310.07554","title":"A Multi-Task Embedder For Retrieval Augmented LLMs","created":"2023-10-11","cited_by_count":64,"area":"rag"},{"id":"2310.15205","title":"DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning","created":"2023-10-23","cited_by_count":64,"area":"rag"},{"id":"2305.01579","title":"Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise","created":"2023-05-02","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":"2405.14431","title":"RaFe: Ranking Feedback Improves Query Rewriting for RAG","created":"2024-05-23","cited_by_count":49,"area":"rag"},{"id":"2403.12582","title":"AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework","created":"2024-03-19","cited_by_count":47,"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":"2310.14696","title":"Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models","created":"2023-10-23","cited_by_count":43,"area":"rag"},{"id":"2407.13998","title":"RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering","created":"2024-07-19","cited_by_count":43,"area":"rag"},{"id":"2407.21059","title":"Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks","created":"2024-07-26","cited_by_count":42,"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":"2306.05212","title":"RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit","created":"2023-06-08","cited_by_count":40,"area":"rag"},{"id":"2311.02962","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","created":"2023-11-06","cited_by_count":40,"area":"rag"},{"id":"2405.13021","title":"IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues","created":"2024-05-15","cited_by_count":40,"area":"rag"},{"id":"2407.01178","title":"$\\text{Memory}^3$: Language Modeling with Explicit Memory","created":"2024-07-01","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":"2401.13256","title":"UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems","created":"2024-01-24","cited_by_count":38,"area":"rag"},{"id":"2405.16420","title":"M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions","created":"2024-05-26","cited_by_count":38,"area":"rag"},{"id":"2405.02580","title":"PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation","created":"2024-05-04","cited_by_count":37,"area":"rag"},{"id":"2409.11242","title":"Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse","created":"2024-09-17","cited_by_count":34,"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":"2407.01102","title":"BERGEN: A Benchmarking Library for Retrieval-Augmented Generation","created":"2024-07-01","cited_by_count":31,"area":"rag"},{"id":"2410.21943","title":"Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications","created":"2024-10-29","cited_by_count":31,"area":"rag"},{"id":"2503.12759","title":"RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning","created":"2025-03-17","cited_by_count":30,"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":"2410.13339","title":"Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval","created":"2024-10-17","cited_by_count":29,"area":"rag"},{"id":"2402.12174","title":"BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence","created":"2024-02-19","cited_by_count":28,"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":"2410.23041","title":"Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval","created":"2024-10-30","cited_by_count":26,"area":"rag"},{"id":"2307.04642","title":"TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction","created":"2023-07-07","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":"2406.09815","title":"Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments","created":"2024-06-14","cited_by_count":25,"area":"rag"},{"id":"2505.02811","title":"Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing","created":"2025-05-05","cited_by_count":25,"area":"rag"},{"id":"2403.18243","title":"Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check","created":"2024-03-27","cited_by_count":24,"area":"rag"},{"id":"2406.14282","title":"Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs","created":"2024-06-20","cited_by_count":24,"area":"rag"},{"id":"2504.18041","title":"RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models","created":"2025-04-25","cited_by_count":24,"area":"rag"},{"id":"2510.16724","title":"A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications","created":"2025-10-19","cited_by_count":22,"area":"rag"},{"id":"2407.01796","title":"Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation","created":"2024-07-01","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":"2405.18111","title":"ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator","created":"2024-05-28","cited_by_count":19,"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":"2410.18141","title":"SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback","created":"2024-10-22","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":"2505.19864","title":"CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models","created":"2025-05-26","cited_by_count":17,"area":"rag"},{"id":"2406.01549","title":"An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation","created":"2024-06-03","cited_by_count":16,"area":"rag"},{"id":"2406.11357","title":"Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities","created":"2024-06-17","cited_by_count":16,"area":"rag"},{"id":"2406.13213","title":"Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata","created":"2024-06-19","cited_by_count":16,"area":"rag"},{"id":"2502.18993","title":"MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering","created":"2025-02-26","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":"2404.17283","title":"Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM","created":"2024-04-26","cited_by_count":15,"area":"rag"},{"id":"2505.07233","title":"DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation","created":"2025-05-12","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":"2312.10904","title":"Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)","created":"2023-12-18","cited_by_count":14,"area":"rag"},{"id":"2406.07348","title":"DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering","created":"2024-06-11","cited_by_count":14,"area":"rag"},{"id":"2406.16989","title":"Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning","created":"2024-06-24","cited_by_count":14,"area":"rag"},{"id":"2510.14337","title":"Stop-RAG: Value-Based Retrieval Control for Iterative RAG","created":"2025-10-16","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":"2406.19251","title":"AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation","created":"2024-06-27","cited_by_count":13,"area":"rag"},{"id":"2410.22353","title":"RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering","created":"2024-10-15","cited_by_count":13,"area":"rag"},{"id":"2409.16146","title":"Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework","created":"2024-09-24","cited_by_count":12,"area":"rag"},{"id":"2504.09593","title":"ControlNET: A Firewall for RAG-based LLM System","created":"2025-04-13","cited_by_count":12,"area":"rag"},{"id":"2512.19134","title":"QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation","created":"2025-12-22","cited_by_count":12,"area":"rag"},{"id":"2601.03746","title":"Whose Facts Win? LLM Source Preferences under Knowledge Conflicts","created":"2026-01-07","cited_by_count":12,"area":"rag"},{"id":"2309.11911","title":"InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models","created":"2023-09-21","cited_by_count":11,"area":"rag"},{"id":"2502.18139","title":"LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers","created":"2025-02-25","cited_by_count":11,"area":"rag"},{"id":"2503.01346","title":"SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph","created":"2025-03-03","cited_by_count":11,"area":"rag"},{"id":"2310.09536","title":"CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering","created":"2023-10-14","cited_by_count":10,"area":"rag"},{"id":"2305.03688","title":"DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition","created":"2023-05-05","cited_by_count":9,"area":"rag"},{"id":"2403.12077","title":"Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions","created":"2024-02-25","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":"2502.06205","title":"C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation","created":"2025-02-10","cited_by_count":9,"area":"rag"},{"id":"2502.10993","title":"RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization","created":"2025-02-16","cited_by_count":9,"area":"rag"},{"id":"2504.11001","title":"ReZero: Enhancing LLM search ability by trying one-more-time","created":"2025-04-15","cited_by_count":9,"area":"rag"},{"id":"2506.20051","title":"Controlled Retrieval-augmented Context Evaluation for Long-form RAG","created":"2025-06-24","cited_by_count":9,"area":"rag"}],"next_cursor":"100"}