{"total":22,"rows":[{"id":"2504.13079","title":"Retrieval-Augmented Generation with Conflicting Evidence","created":"2025-04-17","cited_by_count":63,"area":"rag"},{"id":"2506.08938","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","created":"2025-06-10","cited_by_count":28,"area":"rag"},{"id":"2410.14675","title":"To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts","created":"2024-10-18","cited_by_count":25,"area":"rag"},{"id":"2506.08500","title":"DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs","created":"2025-06-10","cited_by_count":25,"area":"rag"},{"id":"2505.04847","title":"Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards","created":"2025-05-07","cited_by_count":20,"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":"2510.10390","title":"RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models","created":"2025-10-12","cited_by_count":9,"area":"rag"},{"id":"2504.20119","title":"Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets","created":"2025-04-28","cited_by_count":7,"area":"rag"},{"id":"2412.12300","title":"Unanswerability Evaluation for Retrieval Augmented Generation","created":"2024-12-16","cited_by_count":4,"area":"rag"},{"id":"2504.14856","title":"Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation","created":"2025-04-21","cited_by_count":4,"area":"rag"},{"id":"2502.19779","title":"Do Retrieval-Augmented Language Models Adapt to Varying User Needs?","created":"2025-02-27","cited_by_count":2,"area":"rag"},{"id":"2510.00508","title":"Copy-Paste to Mitigate Large Language Model Hallucinations","created":"2025-10-01","cited_by_count":2,"area":"rag"},{"id":"2510.12460","title":"Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation","created":"2025-10-14","cited_by_count":2,"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":"2604.25313","title":"Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models","created":"2026-04-28","cited_by_count":1,"area":"rag"},{"id":"2605.00505","title":"LLM-Oriented Information Retrieval: A Denoising-First Perspective","created":"2026-05-01","cited_by_count":1,"area":"rag"},{"id":"2606.09005","title":"Document-Authored Control-Signal Impersonation: A Low-Cost Indirect Prompt Attack on RAG Safety Boundaries","created":"2026-06-08","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":"2606.12903","title":"X-MADAM-RAG: Diagnosing and Handling Chinese-English Evidence Conflict in Retrieval-Augmented Generation","created":"2026-06-11","cited_by_count":0,"area":"rag"},{"id":"2608.04286","title":"Eliciting Intrinsic Hallucinations in LLMs via Semantically Equivalent Adversarial Attacks","created":"2026-08-04","cited_by_count":0,"area":"rag"},{"id":"2608.16515","title":"When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation","created":"2026-08-17","cited_by_count":0,"area":"rag"},{"id":"2609.04647","title":"CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation","created":"2026-09-04","cited_by_count":0,"area":"rag"}],"next_cursor":null}