{"total":39,"rows":[{"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":"2310.01558","title":"Making Retrieval-Augmented Language Models Robust to Irrelevant Context","created":"2023-10-02","cited_by_count":369,"area":"rag"},{"id":"2501.00309","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","created":"2024-12-31","cited_by_count":172,"area":"rag"},{"id":"2402.14409","title":"Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models","created":"2024-02-22","cited_by_count":93,"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":"2402.11782","title":"What Evidence Do Language Models Find Convincing?","created":"2024-02-19","cited_by_count":75,"area":"rag"},{"id":"2504.13079","title":"Retrieval-Augmented Generation with Conflicting Evidence","created":"2025-04-17","cited_by_count":63,"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":"2406.13805","title":"WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia","created":"2024-06-19","cited_by_count":44,"area":"rag"},{"id":"2410.13509","title":"RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards","created":"2024-10-17","cited_by_count":33,"area":"rag"},{"id":"2404.16032","title":"Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents","created":"2024-04-24","cited_by_count":27,"area":"rag"},{"id":"2309.17415","title":"Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts","created":"2023-09-29","cited_by_count":23,"area":"rag"},{"id":"2408.03297","title":"KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models","created":"2024-08-06","cited_by_count":20,"area":"rag"},{"id":"2509.23519","title":"ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search","created":"2025-09-27","cited_by_count":18,"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":"2511.10375","title":"TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs","created":"2025-11-13","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":"2409.10955","title":"Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style","created":"2024-09-17","cited_by_count":11,"area":"rag"},{"id":"2502.15543","title":"ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation","created":"2025-02-21","cited_by_count":11,"area":"rag"},{"id":"2410.05162","title":"Deciphering the Interplay of Parametric and Non-parametric Memory in Retrieval-augmented Language Models","created":"2024-10-07","cited_by_count":10,"area":"rag"},{"id":"2406.11830","title":"Language Modeling with Editable External Knowledge","created":"2024-06-17","cited_by_count":9,"area":"rag"},{"id":"2410.15107","title":"Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models","created":"2024-10-19","cited_by_count":8,"area":"rag"},{"id":"2408.04414","title":"Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning","created":"2024-08-08","cited_by_count":6,"area":"rag"},{"id":"2502.17390","title":"Mitigating Bias in RAG: Controlling the Embedder","created":"2025-02-24","cited_by_count":6,"area":"rag"},{"id":"2507.21544","title":"MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation","created":"2025-07-29","cited_by_count":6,"area":"rag"},{"id":"2410.16843","title":"Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning","created":"2024-10-22","cited_by_count":4,"area":"rag"},{"id":"2606.27786","title":"SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation","created":"2026-06-26","cited_by_count":3,"area":"rag"},{"id":"2405.20680","title":"Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models","created":"2024-05-31","cited_by_count":2,"area":"rag"},{"id":"2604.09174","title":"Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG","created":"2026-04-10","cited_by_count":2,"area":"rag"},{"id":"2605.27157","title":"Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs","created":"2026-05-26","cited_by_count":2,"area":"rag"},{"id":"2607.26339","title":"RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning","created":"2026-07-28","cited_by_count":2,"area":"rag"},{"id":"2604.12138","title":"Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions","created":"2026-04-13","cited_by_count":1,"area":"rag"},{"id":"2608.07933","title":"EvoTrustRAG: Evolution-Aware Conflict Attribution and Evidence Handling for Reliable Retrieval-Augmented Generation","created":"2026-08-08","cited_by_count":1,"area":"rag"},{"id":"2403.14197","title":"Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering","created":"2024-03-21","cited_by_count":0,"area":"rag"},{"id":"2606.23695","title":"Quantifying Prior Dominance in RAG Systems","created":"2026-04-29","cited_by_count":0,"area":"rag"},{"id":"2607.00570","title":"Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation","created":"2026-07-01","cited_by_count":0,"area":"rag"},{"id":"2607.22625","title":"TokenMem: Faithful Knowledge Injection for Frozen LLMs","created":"2026-06-17","cited_by_count":0,"area":"rag"},{"id":"2609.00543","title":"Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generation","created":"2026-09-01","cited_by_count":0,"area":"rag"},{"id":"2609.07075","title":"A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems","created":"2026-09-07","cited_by_count":0,"area":"rag"}],"next_cursor":null}