Cover Image

Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Guanyu, Yan, Yibo, Zou, Xin, Wang, Kun, Liu, Aiwei, Hu, Xuming
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models
by: Zheng, Kening, et al.
Published: (2024)

Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering
by: Liu, Shuliang, et al.
Published: (2026)

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models
by: Zou, Xin, et al.
Published: (2024)

Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation
by: Jia, Sihang, et al.
Published: (2026)

Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
by: Deng, Haolin, et al.
Published: (2026)

When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs
by: Cao, Fanpu, et al.
Published: (2026)

Visual Attention Drifts,but Anchors Hold:Mitigating Hallucination in Multimodal Large Language Models via Cross-Layer Visual Anchors
by: Yang, Chengxu, et al.
Published: (2026)

Tracing and Mitigating Hallucinations in Multimodal LLMs via Dynamic Attention Localization
by: Yang, Tiancheng, et al.
Published: (2025)

Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating
by: Cheng, Zhe, et al.
Published: (2026)

Mitigating Object Hallucination via Concentric Causal Attention
by: Xing, Yun, et al.
Published: (2024)

Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow
by: Zhao, Jianfei, et al.
Published: (2025)

SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
by: Chen, Junkai, et al.
Published: (2025)

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
by: You, Liangliang, et al.
Published: (2025)

The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio
by: Leng, Sicong, et al.
Published: (2024)

Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
by: Zhu, Younan, et al.
Published: (2025)

MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models
by: Hu, Lulu, et al.
Published: (2026)

Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
by: He, Zhentao, et al.
Published: (2025)

Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration
by: Fazli, Mehrdad, et al.
Published: (2025)

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models
by: Zhou, Haoran, et al.
Published: (2025)

V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
by: Sun, Nan, et al.
Published: (2025)

ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models
by: Chen, Junzhe, et al.
Published: (2024)

Dynamic Multimodal Activation Steering for Hallucination Mitigation in Large Vision-Language Models
by: Yin, Jianghao, et al.
Published: (2026)

A Visual Semantic Adaptive Watermark grounded by Prefix-Tuning for Large Vision-Language Model
by: Zheng, Qi, et al.
Published: (2026)

Sharp Eyes and Memory for VideoLLMs: Information-Aware Visual Token Pruning for Efficient and Reliable VideoLLM Reasoning
by: Qin, Jialong, et al.
Published: (2025)

Cross-Modal Attention Calibration for LVLM Hallucination Mitigation
by: Li, Jiaming, et al.
Published: (2025)

MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
by: Li, Jiale, et al.
Published: (2025)

Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization
by: Fu, Yuhan, et al.
Published: (2024)

Temporal Gains, Spatial Costs: Revisiting Video Fine-Tuning in Multimodal Large Language Models
by: Zhang, Linghao, et al.
Published: (2026)

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization
by: Wu, Jiulong, et al.
Published: (2025)

Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
by: Zhang, Yuanhong, et al.
Published: (2026)

LISA: A Layer-wise Integration and Suppression Approach for Hallucination Mitigation in Multimodal Large Language Models
by: Guo, Zhihui, et al.
Published: (2025)

Watch Closely: Mitigating Object Hallucinations in Large Vision-Language Models with Disentangled Decoding
by: Ma, Ruiqi, et al.
Published: (2025)

RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning
by: Lyu, Yuanhuiyi, et al.
Published: (2025)

SchröMind: Mitigating Hallucinations in Multimodal Large Language Models via Solving the Schrödinger Bridge Problem
by: Shi, Ziqiang, et al.
Published: (2026)

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations
by: Lu, Yifan, et al.
Published: (2025)

When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding
by: Shu, Yan, et al.
Published: (2025)

Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens
by: Zheng, Haohan, et al.
Published: (2025)

Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
by: Tan, Shiwei, et al.
Published: (2026)

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
by: Tang, Feilong, et al.
Published: (2025)

Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation
by: Li, Qiming, et al.
Published: (2025)