Cover Image

PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Zhaopan, Zhou, Pengfei, Tang, Weidong, Ai, Jiaxin, Zhao, Wangbo, Wang, Kai, Peng, Xiaojiang, Shao, Wenqi, Yao, Hongxun, Zhang, Kaipeng
Format:	Preprint
Published:	2025
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

MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification
by: Xu, Zhaopan, et al.
Published: (2025)

MDK12-Bench: A Multi-Discipline Benchmark for Evaluating Reasoning in Multimodal Large Language Models
by: Zhou, Pengfei, et al.
Published: (2025)

MDK12-Bench: A Comprehensive Evaluation of Multimodal Large Language Models on Multidisciplinary Exams
by: Zhou, Pengfei, et al.
Published: (2025)

DAM: Dual Active Learning with Multimodal Foundation Model for Source-Free Domain Adaptation
by: Chen, Xi, et al.
Published: (2025)

Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
by: Ma, Yingzi, et al.
Published: (2024)

Dataset Growth
by: Qin, Ziheng, et al.
Published: (2024)

ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges
by: Ai, Jiaxin, et al.
Published: (2025)

Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation
by: Zhu, Jiankun, et al.
Published: (2024)

Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
by: Qiu, Yansheng, et al.
Published: (2025)

TOFU: A Task of Fictitious Unlearning for LLMs
by: Maini, Pratyush, et al.
Published: (2024)

TP-Eval: Tap Multimodal LLMs' Potential in Evaluation by Customizing Prompts
by: Xie, Yuxuan, et al.
Published: (2024)

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
by: Chen, Mengzhao, et al.
Published: (2024)

BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
by: Xu, Peng, et al.
Published: (2024)

Multi-source Domain Adaptation for Panoramic Semantic Segmentation
by: Jiang, Jing, et al.
Published: (2024)

ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
by: Meng, Fanqing, et al.
Published: (2024)

Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
by: Li, Jiaqi, et al.
Published: (2024)

AI Idea Bench 2025: AI Research Idea Generation Benchmark
by: Qiu, Yansheng, et al.
Published: (2025)

Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping
by: Yang, Yue, et al.
Published: (2024)

ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification
by: He, Yefei, et al.
Published: (2024)

AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models
by: Lian, Zheng, et al.
Published: (2025)

OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models
by: Zheng, Hao, et al.
Published: (2025)

A New Dataset and Benchmark for Grounding Multimodal Misinformation
by: Yang, Bingjian, et al.
Published: (2025)

OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
by: Zhou, Pengfei, et al.
Published: (2024)

MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models
by: Huo, Jiahao, et al.
Published: (2025)

Rethinking Machine Unlearning for Large Language Models
by: Liu, Sijia, et al.
Published: (2024)

SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model
by: Chang, Yifan, et al.
Published: (2025)

ICU-Bench:Benchmarking Continual Unlearning in Multimodal Large Language Models
by: Wang, Yuhang, et al.
Published: (2026)

EA-ViT: Efficient Adaptation for Elastic Vision Transformer
by: Zhu, Chen, et al.
Published: (2025)

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
by: Shao, Wenqi, et al.
Published: (2023)

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
by: Jin, Zhuoran, et al.
Published: (2024)

Machine Unlearning of Pre-trained Large Language Models
by: Yao, Jin, et al.
Published: (2024)

MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning
by: Cheng, Jiali, et al.
Published: (2024)

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
by: Meng, Fanqing, et al.
Published: (2024)

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

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
by: Ying, Kaining, et al.
Published: (2024)

Machine Unlearning in Large Language Models
by: Chen, Kongyang, et al.
Published: (2024)

Machine Unlearning in Large Language Models
by: Gundavarapu, Saaketh Koundinya, et al.
Published: (2024)

SUA: Stealthy Multimodal Large Language Model Unlearning Attack
by: Zhang, Xianren, et al.
Published: (2025)

MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
by: Yao, Xincheng, et al.
Published: (2026)

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
by: Liu, Shuo, et al.
Published: (2024)