MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs

• URL: http://arxiv.org/abs/2606.12809v1
• Date: Thu, 11 Jun 2026 02:09:26 GMT
• Title: MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
• Abstract summary: Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important.<n>In practice, these requests often arrive sequentially over time, giving rise to the challenging problem of MLLM Lifelong Unlearning.<n>We introduce the MLUBench, a large-scale and comprehensive benchmark featuring 127 entities across 9 classes under lifelong unlearning requests.
• Score: 70.42738224240857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content. In practice, these requests often arrive sequentially over time, giving rise to the challenging problem of MLLM Lifelong Unlearning. However, most existing benchmarks are limited in scale and scope, failing to capture the complexities of MLLM lifelong unlearning. To fill this gap, we introduce the MLUBench, a large-scale and comprehensive benchmark featuring 127 entities across 9 classes under lifelong unlearning requests. We perform extensive experiments using MLUBench and reveal that existing unlearning methods suffer from severe, cumulative degradation. More critically, we further identify the unique challenge of this problem: unlike in unimodal models, MLLM lifelong unlearning is constrained by the need to preserve multimodal alignment. Continually unlearning from one modality could degrade the entire model. To alleviate this challenge, we propose LUMoE, an effective method. Experiments demonstrate that LUMoE significantly mitigates the degradation problem faced by baselines. The source code and the MLUBench dataset are open-sourced in https://github.com/lihe-maxsize/Lifelong_Unlearning_main.

Related papers

• RETLLM: Training and Data-Free MLLMs for Multimodal Information Retrieval [2.2125276321198677]
  Multimodal information retrieval (MMIR) has gained attention for its flexibility in handling text, images, or mixed queries and candidates.<n>Recent breakthroughs in multimodal large language models (MLLMs) boost MMIR performance by incorporating MLLM knowledge under the contrastive finetuning framework.<n>We introduce a novel framework, RetLLM, designed to query MLLMs for MMIR in a training- and data-free manner.
  arXiv  Detail & Related papers  (2026-02-25T10:31:32Z)
• Improve MLLM Benchmark Efficiency through Interview [41.51050382338298]
  Full-coverage Q&A testing on large-scale data is resource-intensive and time-consuming.<n>We propose the MLLM Interview strategy, which aims to quickly obtain MLLM performance metrics by quizzing fewer question.
  arXiv  Detail & Related papers  (2025-06-01T07:51:15Z)
• PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models [27.338242898495448]
  Multimodal large language models (MLLMs) have achieved remarkable success in vision-language tasks.<n>Their reliance on vast, internet-sourced data raises significant privacy and security concerns.<n>Machine unlearning (MU) has emerged as a critical technique to address these issues.<n>PEBench is a novel benchmark designed to facilitate a thorough assessment of MU in MLLMs.
  arXiv  Detail & Related papers  (2025-03-16T15:26:20Z)
• Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation [55.21013307734612]
  AoPS-Instruct is a dataset of more than 600,000 high-quality QA pairs.<n>LiveAoPSBench is an evolving evaluation set with timestamps, derived from the latest forum data.<n>Our work presents a scalable approach to creating and maintaining large-scale, high-quality datasets for advanced math reasoning.
  arXiv  Detail & Related papers  (2025-01-24T06:39:38Z)
• MoExtend: Tuning New Experts for Modality and Task Extension [61.29100693866109]
  MoExtend is an effective framework designed to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models. MoExtend seamlessly integrates new experts into pre-trained MoE models, endowing them with novel knowledge without the need to tune pretrained models.
  arXiv  Detail & Related papers  (2024-08-07T02:28:37Z)
• UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models [0.42832989850721054]
  Multimodal Entities Linking (MEL) is a crucial task that aims at linking ambiguous mentions within multimodal contexts to referent entities in a multimodal knowledge base, such as Wikipedia. Existing methods overcomplicate the MEL task and overlook the visual semantic information, which makes them costly and hard to scale. We propose UniMEL, a unified framework which establishes a new paradigm to process multimodal entity linking tasks using Large Language Models.
  arXiv  Detail & Related papers  (2024-07-23T03:58:08Z)
• Efficient Multimodal Large Language Models: A Survey [60.7614299984182]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. The extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. This survey provides a comprehensive and systematic review of the current state of efficient MLLMs.
  arXiv  Detail & Related papers  (2024-05-17T12:37:10Z)
• Online Cascade Learning for Efficient Inference over Streams [9.516197133796437]
  Large Language Models (LLMs) have a natural role in answering complex queries about data streams. We propose online cascade learning, the first approach to address this challenge. We formulate the task of learning cascades online as an imitation-learning problem.
  arXiv  Detail & Related papers  (2024-02-07T01:46:50Z)
• u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model [17.3535277338312]
  u-LLaVA is an innovative unifying multi-task framework that integrates pixel, regional, and global features to refine the perceptual faculties of MLLMs. This work contributes a novel mask-based multi-task dataset comprising 277K samples, crafted to challenge and assess the fine-grained perception capabilities of MLLMs.
  arXiv  Detail & Related papers  (2023-11-09T13:18:27Z)
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models [111.51612340032052]
  Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks.<n>This paper presents the first comprehensive MLLM Evaluation benchmark MME.<n>It measures both perception and cognition abilities on a total of 14 subtasks.
  arXiv  Detail & Related papers  (2023-06-23T09:22:36Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.