Cover Image

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Xinlong, Zhang, Yuanxing, Liu, Qiang, Wu, Junfei, Zhang, Fuzheng, Tan, Tieniu
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
by: Wu, Junfei, et al.
Published: (2024)

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

Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models
by: Zhang, Ce, et al.
Published: (2025)

Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding
by: Wang, Chao, et al.
Published: (2025)

Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
by: Wang, Xintong, et al.
Published: (2024)

Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding
by: Min, Kyungmin, et al.
Published: (2024)

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models
by: Lee, Yi-Lun, et al.
Published: (2024)

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

Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD)
by: Manevich, Avshalom, et al.
Published: (2024)

Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -
by: Fieback, Laura, et al.
Published: (2025)

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
by: Wang, Weihang, et al.
Published: (2025)

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

VEGAS: Mitigating Hallucinations in Large Vision-Language Models via Vision-Encoder Attention Guided Adaptive Steering
by: Wang, Zihu, et al.
Published: (2025)

PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model
by: Arif, Kazi Hasan Ibn, et al.
Published: (2025)

Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models
by: Li, Bin, et al.
Published: (2025)

A Unified Hallucination Mitigation Framework for Large Vision-Language Models
by: Chang, Yue, et al.
Published: (2024)

Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
by: Deng, Ailin, et al.
Published: (2024)

Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
by: An, Wenbin, et al.
Published: (2024)

HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding
by: Yuan, Fan, et al.
Published: (2024)

VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
by: Huang, Han, et al.
Published: (2024)

HAIC: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models
by: Wang, Xiao, et al.
Published: (2025)

Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs
by: Fang, Hao, et al.
Published: (2025)

Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding
by: Chen, Boqi, et al.
Published: (2026)

Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
by: Zhou, Yiyang, et al.
Published: (2023)

SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding
by: Wu, Chang-Hsun, et al.
Published: (2025)

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention
by: Ye, Zekai, et al.
Published: (2025)

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models
by: Wan, Zifu, et al.
Published: (2025)

Mitigating Multilingual Hallucination in Large Vision-Language Models
by: Qu, Xiaoye, et al.
Published: (2024)

Mavors: Multi-granularity Video Representation for Multimodal Large Language Model
by: Shi, Yang, et al.
Published: (2025)

Chatting with Images for Introspective Visual Thinking
by: Wu, Junfei, et al.
Published: (2026)

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
by: Agrawal, Aakriti, 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)

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

ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM
by: Wang, Yujun, et al.
Published: (2025)

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
by: Zhong, Weihong, et al.
Published: (2024)

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

MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
by: Wang, Chenxi, et al.
Published: (2024)

YARD: Y-Architecture Register Decoding for Efficient Hallucination Mitigation in Large Vision-Language Models
by: Chen, Ting, et al.
Published: (2026)

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
by: Xing, Shangyu, et al.
Published: (2024)

ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models
by: Kim, Minchan, et al.
Published: (2024)