{"total":158,"rows":[{"id":"2311.16922","title":"Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding","created":"2023-11-28","cited_by_count":688,"area":"mllm"},{"id":"2311.17911","title":"OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation","created":"2023-11-29","cited_by_count":478,"area":"mllm"},{"id":"2312.00849","title":"RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback","created":"2023-12-01","cited_by_count":412,"area":"mllm"},{"id":"2402.04788","title":"MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark","created":"2024-02-07","cited_by_count":372,"area":"mllm"},{"id":"2401.16420","title":"InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model","created":"2024-01-29","cited_by_count":326,"area":"mllm"},{"id":"2311.16839","title":"Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization","created":"2023-11-28","cited_by_count":225,"area":"mllm"},{"id":"2407.21771","title":"Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs","created":"2024-07-31","cited_by_count":179,"area":"mllm"},{"id":"2403.00425","title":"HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding","created":"2024-03-01","cited_by_count":174,"area":"mllm"},{"id":"2411.16724","title":"Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens","created":"2024-11-23","cited_by_count":143,"area":"mllm"},{"id":"2402.12451","title":"The Revolution of Multimodal Large Language Models: A Survey","created":"2024-02-19","cited_by_count":137,"area":"mllm"},{"id":"2311.03287","title":"Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges","created":"2023-11-06","cited_by_count":131,"area":"mllm"},{"id":"2311.13614","title":"HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data","created":"2023-11-22","cited_by_count":126,"area":"mllm"},{"id":"2403.09513","title":"AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting","created":"2024-03-14","cited_by_count":125,"area":"mllm"},{"id":"2402.18476","title":"IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding","created":"2024-02-28","cited_by_count":118,"area":"mllm"},{"id":"2410.02762","title":"Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations","created":"2024-10-03","cited_by_count":104,"area":"mllm"},{"id":"2408.02032","title":"Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models","created":"2024-08-04","cited_by_count":94,"area":"mllm"},{"id":"2406.16338","title":"VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models","created":"2024-06-24","cited_by_count":90,"area":"mllm"},{"id":"2410.11779","title":"MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation","created":"2024-10-15","cited_by_count":88,"area":"mllm"},{"id":"2405.14622","title":"Calibrated Self-Rewarding Vision Language Models","created":"2024-05-23","cited_by_count":78,"area":"mllm"},{"id":"2403.03170","title":"SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection","created":"2024-03-05","cited_by_count":74,"area":"mllm"},{"id":"2406.12718","title":"Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention","created":"2024-06-18","cited_by_count":74,"area":"mllm"},{"id":"2410.03577","title":"Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models","created":"2024-10-04","cited_by_count":72,"area":"mllm"},{"id":"2411.15296","title":"MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs","created":"2024-11-22","cited_by_count":72,"area":"mllm"},{"id":"2402.14683","title":"Visual Hallucinations of Multi-modal Large Language Models","created":"2024-02-22","cited_by_count":68,"area":"mllm"},{"id":"2412.03735","title":"VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding","created":"2024-12-04","cited_by_count":68,"area":"mllm"},{"id":"2410.15778","title":"Reducing Hallucinations in Vision-Language Models via Latent Space Steering","created":"2024-10-21","cited_by_count":59,"area":"mllm"},{"id":"2409.16597","title":"EventHallusion: Diagnosing Event Hallucinations in Video LLMs","created":"2024-09-25","cited_by_count":57,"area":"mllm"},{"id":"2311.16479","title":"Mitigating Hallucination in Visual Language Models with Visual Supervision","created":"2023-11-27","cited_by_count":49,"area":"mllm"},{"id":"2402.13220","title":"How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts","created":"2024-02-20","cited_by_count":47,"area":"mllm"},{"id":"2412.13817","title":"Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection","created":"2024-12-18","cited_by_count":46,"area":"mllm"},{"id":"2403.11116","title":"PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset","created":"2024-03-17","cited_by_count":45,"area":"mllm"},{"id":"2501.12327","title":"VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model","created":"2025-01-21","cited_by_count":44,"area":"mllm"},{"id":"2502.06130","title":"Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models","created":"2025-02-10","cited_by_count":44,"area":"mllm"},{"id":"2505.16652","title":"Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding","created":"2025-05-22","cited_by_count":43,"area":"mllm"},{"id":"2503.06486","title":"PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training","created":"2025-03-09","cited_by_count":38,"area":"mllm"},{"id":"2503.06169","title":"Treble Counterfactual VLMs: A Causal Approach to Hallucination","created":"2025-03-08","cited_by_count":35,"area":"mllm"},{"id":"2405.15356","title":"Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization","created":"2024-05-24","cited_by_count":34,"area":"mllm"},{"id":"2504.13169","title":"Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling","created":"2025-04-17","cited_by_count":33,"area":"mllm"},{"id":"2311.14552","title":"Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models","created":"2023-11-24","cited_by_count":31,"area":"mllm"},{"id":"2501.16629","title":"CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs","created":"2025-01-28","cited_by_count":31,"area":"mllm"},{"id":"2404.05046","title":"FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback","created":"2024-04-07","cited_by_count":30,"area":"mllm"},{"id":"2411.15268","title":"ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models","created":"2024-11-22","cited_by_count":30,"area":"mllm"},{"id":"2507.12455","title":"Mitigating Object Hallucinations via Sentence-Level Early Intervention","created":"2025-07-16","cited_by_count":28,"area":"mllm"},{"id":"2412.03665","title":"Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis","created":"2024-12-04","cited_by_count":23,"area":"mllm"},{"id":"2503.08342","title":"Attention Reallocation: Towards Zero-cost and Controllable Hallucination Mitigation of MLLMs","created":"2025-03-11","cited_by_count":23,"area":"mllm"},{"id":"2404.17672","title":"BlenderAlchemy: Editing 3D Graphics with Vision-Language Models","created":"2024-04-26","cited_by_count":22,"area":"mllm"},{"id":"2401.15071","title":"From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities","created":"2024-01-26","cited_by_count":19,"area":"mllm"},{"id":"2412.18826","title":"RapGuard: Safeguarding Multimodal Large Language Models via Rationale-aware Defensive Prompting","created":"2024-12-25","cited_by_count":19,"area":"mllm"},{"id":"2407.13442","title":"BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models","created":"2024-07-18","cited_by_count":18,"area":"mllm"},{"id":"2404.14715","title":"FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction","created":"2024-04-23","cited_by_count":17,"area":"mllm"},{"id":"2501.06553","title":"VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification","created":"2025-01-11","cited_by_count":17,"area":"mllm"},{"id":"2504.05810","title":"PaMi-VDPO: Mitigating Video Hallucinations by Prompt-Aware Multi-Instance Video Preference Learning","created":"2025-04-08","cited_by_count":17,"area":"mllm"},{"id":"2505.19678","title":"Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs","created":"2025-05-26","cited_by_count":17,"area":"mllm"},{"id":"2504.21051","title":"Multimodal Large Language Models for Medicine: A Comprehensive Survey","created":"2025-04-29","cited_by_count":16,"area":"mllm"},{"id":"2507.00898","title":"ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models","created":"2025-07-01","cited_by_count":16,"area":"mllm"},{"id":"2410.04734","title":"TLDR: Token-Level Detective Reward Model for Large Vision Language Models","created":"2024-10-07","cited_by_count":15,"area":"mllm"},{"id":"2508.12815","title":"Learning to Steer: Input-dependent Steering for Multimodal LLMs","created":"2025-08-18","cited_by_count":15,"area":"mllm"},{"id":"2403.14401","title":"Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination","created":"2024-03-21","cited_by_count":14,"area":"mllm"},{"id":"2405.19186","title":"MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification","created":"2024-05-29","cited_by_count":13,"area":"mllm"},{"id":"2509.21789","title":"Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow","created":"2025-09-26","cited_by_count":13,"area":"mllm"},{"id":"2403.16167","title":"ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models","created":"2024-03-24","cited_by_count":12,"area":"mllm"},{"id":"2407.11422","title":"Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models","created":"2024-07-16","cited_by_count":12,"area":"mllm"},{"id":"2411.15839","title":"VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding","created":"2024-11-24","cited_by_count":12,"area":"mllm"},{"id":"2412.06775","title":"Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models","created":"2024-12-09","cited_by_count":12,"area":"mllm"},{"id":"2505.17529","title":"Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding","created":"2025-05-23","cited_by_count":12,"area":"mllm"},{"id":"2503.10602","title":"TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Intervention","created":"2025-03-13","cited_by_count":11,"area":"mllm"},{"id":"2503.10183","title":"Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding","created":"2025-03-13","cited_by_count":10,"area":"mllm"},{"id":"2504.20468","title":"Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception","created":"2025-04-29","cited_by_count":10,"area":"mllm"},{"id":"2402.15300","title":"Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding","created":"2024-02-23","cited_by_count":8,"area":"mllm"},{"id":"2405.17821","title":"RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models","created":"2024-05-28","cited_by_count":8,"area":"mllm"},{"id":"2409.06485","title":"Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding","created":"2024-09-10","cited_by_count":8,"area":"mllm"},{"id":"2412.11050","title":"RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models","created":"2024-12-15","cited_by_count":8,"area":"mllm"},{"id":"2505.12826","title":"Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering","created":"2025-05-19","cited_by_count":8,"area":"mllm"},{"id":"2410.09962","title":"LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models","created":"2024-10-13","cited_by_count":7,"area":"mllm"},{"id":"2411.17265","title":"Systematic Reward Gap Optimization for Mitigating VLM Hallucinations","created":"2024-11-26","cited_by_count":7,"area":"mllm"},{"id":"2503.14895","title":"Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations","created":"2025-03-19","cited_by_count":7,"area":"mllm"},{"id":"2505.12343","title":"Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models","created":"2025-05-18","cited_by_count":7,"area":"mllm"},{"id":"2508.20181","title":"Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization","created":"2025-08-27","cited_by_count":7,"area":"mllm"},{"id":"2510.12750","title":"VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage","created":"2025-10-14","cited_by_count":7,"area":"mllm"},{"id":"2510.16596","title":"SHIELD: Suppressing Hallucinations In LVLM Encoders via Bias and Vulnerability Defense","created":"2025-10-18","cited_by_count":7,"area":"mllm"},{"id":"2512.18813","title":"Revealing Perception and Generation Dynamics in LVLMs: Mitigating Hallucinations via Validated Dominance Correction","created":"2025-12-21","cited_by_count":7,"area":"mllm"},{"id":"2602.24041","title":"Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation","created":"2026-02-27","cited_by_count":7,"area":"mllm"},{"id":"2404.10332","title":"Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning","created":"2024-04-16","cited_by_count":6,"area":"mllm"},{"id":"2405.17475","title":"How Culturally Aware are Vision-Language Models?","created":"2024-05-24","cited_by_count":6,"area":"mllm"},{"id":"2511.17254","title":"Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats","created":"2025-11-21","cited_by_count":6,"area":"mllm"},{"id":"2410.06795","title":"From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models","created":"2024-10-09","cited_by_count":5,"area":"mllm"},{"id":"2503.20504","title":"UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Models","created":"2025-03-26","cited_by_count":5,"area":"mllm"},{"id":"2505.19498","title":"Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models","created":"2025-05-26","cited_by_count":5,"area":"mllm"},{"id":"2506.17901","title":"PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs","created":"2025-06-22","cited_by_count":5,"area":"mllm"},{"id":"2603.10470","title":"Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression","created":"2026-03-11","cited_by_count":5,"area":"mllm"},{"id":"2412.02946","title":"Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis","created":"2024-12-04","cited_by_count":4,"area":"mllm"},{"id":"2412.07518","title":"Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios","created":"2024-12-10","cited_by_count":4,"area":"mllm"},{"id":"2412.15739","title":"VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models","created":"2024-12-20","cited_by_count":4,"area":"mllm"},{"id":"2505.19474","title":"Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models","created":"2025-05-26","cited_by_count":4,"area":"mllm"},{"id":"2509.21997","title":"Exposing Hallucinations To Suppress Them: VLMs Representation Editing With Generative Anchors","created":"2025-09-26","cited_by_count":4,"area":"mllm"},{"id":"2601.00659","title":"CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models","created":"2026-01-02","cited_by_count":4,"area":"mllm"},{"id":"2603.19765","title":"FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs","created":"2026-03-20","cited_by_count":4,"area":"mllm"},{"id":"2604.06714","title":"Steering the Verifiability of Multimodal AI Hallucinations","created":"2026-04-08","cited_by_count":4,"area":"mllm"},{"id":"2311.07594","title":"How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model","created":"2023-11-10","cited_by_count":3,"area":"mllm"},{"id":"2409.13612","title":"FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs","created":"2024-09-20","cited_by_count":3,"area":"mllm"}],"next_cursor":"100"}