{"total":43,"rows":[{"id":"2208.03299","title":"Atlas: Few-shot Learning with Retrieval Augmented Language Models","created":"2022-08-05","cited_by_count":952,"area":"rag"},{"id":"2408.08921","title":"Graph Retrieval-Augmented Generation: A Survey","created":"2024-08-15","cited_by_count":324,"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":"2507.03724","title":"MemOS: A Memory OS for AI System","created":"2025-07-04","cited_by_count":138,"area":"rag"},{"id":"2405.16506","title":"GRAG: Graph Retrieval-Augmented Generation","created":"2024-05-26","cited_by_count":78,"area":"rag"},{"id":"2205.14704","title":"Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning","created":"2022-05-29","cited_by_count":36,"area":"rag"},{"id":"2309.17050","title":"Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models","created":"2023-09-29","cited_by_count":35,"area":"rag"},{"id":"2404.02319","title":"Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization","created":"2024-04-02","cited_by_count":30,"area":"rag"},{"id":"2202.00535","title":"Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning","created":"2022-02-01","cited_by_count":25,"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":"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":"2407.01972","title":"MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation","created":"2024-07-02","cited_by_count":13,"area":"rag"},{"id":"2403.08904","title":"Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics","created":"2024-03-13","cited_by_count":9,"area":"rag"},{"id":"2404.01554","title":"FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion","created":"2024-04-02","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":"2505.16237","title":"Align-GRAG: Anchor and Rationale Guided Dual Alignment for Graph Retrieval-Augmented Generation","created":"2025-05-22","cited_by_count":9,"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":"2307.12798","title":"RRAML: Reinforced Retrieval Augmented Machine Learning","created":"2023-07-24","cited_by_count":6,"area":"rag"},{"id":"2409.15699","title":"Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation","created":"2024-09-24","cited_by_count":6,"area":"rag"},{"id":"2504.10529","title":"HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations","created":"2025-04-12","cited_by_count":6,"area":"rag"},{"id":"2409.13385","title":"Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey","created":"2024-09-20","cited_by_count":5,"area":"rag"},{"id":"2504.17200","title":"A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation","created":"2025-04-24","cited_by_count":5,"area":"rag"},{"id":"2405.19670","title":"One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models","created":"2024-05-30","cited_by_count":4,"area":"rag"},{"id":"2604.12610","title":"Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs","created":"2026-04-14","cited_by_count":3,"area":"rag"},{"id":"2410.03461","title":"Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation","created":"2024-10-04","cited_by_count":2,"area":"rag"},{"id":"2502.20245","title":"From Retrieval to Generation: Comparing Different Approaches","created":"2025-02-27","cited_by_count":2,"area":"rag"},{"id":"2507.03868","title":"From Query to Explanation: Uni-RAG for Multi-Modal Retrieval-Augmented Learning in STEM","created":"2025-07-05","cited_by_count":2,"area":"rag"},{"id":"2510.01363","title":"Retrieval-Augmented Framework for LLM-Based Clinical Decision Support","created":"2025-10-01","cited_by_count":2,"area":"rag"},{"id":"2511.04072","title":"Plan of Knowledge: Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering","created":"2025-11-06","cited_by_count":2,"area":"rag"},{"id":"2605.26356","title":"In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspective","created":"2026-05-25","cited_by_count":2,"area":"rag"},{"id":"2312.14327","title":"Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion","created":"2023-12-21","cited_by_count":1,"area":"rag"},{"id":"2406.19309","title":"Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders","created":"2024-06-27","cited_by_count":1,"area":"rag"},{"id":"2407.03955","title":"Meta-prompting Optimized Retrieval-augmented Generation","created":"2024-07-04","cited_by_count":1,"area":"rag"},{"id":"2411.11895","title":"Deploying Large Language Models With Retrieval Augmented Generation","created":"2024-11-07","cited_by_count":1,"area":"rag"},{"id":"2502.12502","title":"Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts","created":"2025-02-18","cited_by_count":1,"area":"rag"},{"id":"2603.18009","title":"How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding","created":"2026-02-23","cited_by_count":1,"area":"rag"},{"id":"2406.03790","title":"End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction","created":"2024-06-06","cited_by_count":0,"area":"rag"},{"id":"2509.16508","title":"Federated Learning with Ad-hoc Adapter Insertions: The Case of Soft-Embeddings for Training Classifier-as-Retriever","created":"2025-09-20","cited_by_count":0,"area":"rag"},{"id":"2603.06593","title":"Hierarchical Embedding Fusion for Retrieval-Augmented Code Generation","created":"2026-02-04","cited_by_count":0,"area":"rag"},{"id":"2604.18026","title":"RASP-Tuner: Retrieval-Augmented Soft Prompts for Context-Aware Black-Box Optimization in Non-Stationary Environments","created":"2026-04-20","cited_by_count":0,"area":"rag"},{"id":"2605.07517","title":"LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation","created":"2026-05-08","cited_by_count":0,"area":"rag"},{"id":"2608.02560","title":"Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection","created":"2026-08-03","cited_by_count":0,"area":"rag"},{"id":"2609.18317","title":"Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering","created":"2026-09-16","cited_by_count":0,"area":"rag"}],"next_cursor":null}