# MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing

arXiv: https://arxiv.org/abs/2402.14835 · HTML: https://mlsanity.com/paper/2402.14835/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Image editing and quality assessment, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Image editing and quality assessment](https://mlsanity.com/mllm/image-editing-and-quality-assessment/)

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://mlsanity.com/paper/2305.03726/) (2023)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://mlsanity.com/paper/2305.12740/) (2023)
- [Modifying Memories in Transformer Models](https://mlsanity.com/paper/2012.00363/) (2020)
- [Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?](https://mlsanity.com/paper/2302.11713/) (2023)
- [Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities](https://mlsanity.com/paper/2302.11154/) (2023)
- [SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation](https://mlsanity.com/paper/2209.15323/) (2022)
- [Repairing Neural Networks by Leaving the Right Past Behind](https://mlsanity.com/paper/2207.04806/) (2022)
- [Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!](https://mlsanity.com/paper/2306.03932/) (2023)
- [Fine-Grained Classification with Noisy Labels](https://mlsanity.com/paper/2303.02404/) (2023)
- [Personalizing Text-to-Image Generation via Aesthetic Gradients](https://mlsanity.com/paper/2209.12330/) (2022)
- [Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia](https://mlsanity.com/paper/2209.10474/) (2022)
- [MyStyle++: A Controllable Personalized Generative Prior](https://mlsanity.com/paper/2306.04865/) (2023)
- [Benchmarking Diverse-Modal Entity Linking with Generative Models](https://mlsanity.com/paper/2305.17337/) (2023)
- [Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval](https://mlsanity.com/paper/2112.05917/) (2021)
- [Few-shot Classification via Ensemble Learning with Multi-Order Statistics](https://mlsanity.com/paper/2305.00454/) (2023)

## Built on by (most cited first, 25 of 25)

- [Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models](https://mlsanity.com/paper/2405.12523/) (2024)
- [A Survey on Evaluation of Multimodal Large Language Models](https://mlsanity.com/paper/2408.15769/) (2024)
- [Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset](https://mlsanity.com/paper/2411.03554/) (2024)
- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [A Survey on Multimodal Benchmarks: In the Era of Large AI Models](https://mlsanity.com/paper/2409.18142/) (2024)
- [MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge](https://mlsanity.com/paper/2502.19870/) (2025)
- [MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency](https://mlsanity.com/paper/2406.13219/) (2024)
- [Can Editing LLMs Inject Harm?](https://mlsanity.com/paper/2407.20224/) (2024)
- [Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEdit](https://mlsanity.com/paper/2408.09916/) (2024)
- [Model Editing for LLMs4Code: How Far are We?](https://mlsanity.com/paper/2411.06638/) (2024)
- [Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models](https://mlsanity.com/paper/2411.12790/) (2024)
- [OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models](https://mlsanity.com/paper/2510.22535/) (2025)
- [MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models](https://mlsanity.com/paper/2510.19457/) (2025)
- [PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models](https://mlsanity.com/paper/2503.12545/) (2025)
- [E-SAM: Training-Free Segment Every Entity Model](https://mlsanity.com/paper/2503.12094/) (2025)
- [SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services](https://mlsanity.com/paper/2505.23065/) (2025)
- [MemEIC: A Step Toward Continual and Compositional Knowledge Editing](https://mlsanity.com/paper/2510.25798/) (2025)
- [Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs](https://mlsanity.com/paper/2509.05714/) (2025)
- [Generalizable Multimodal Large Language Model Editing via Invariant Trajectory Learning](https://mlsanity.com/paper/2601.19700/) (2026)
- [Uncovering Entity Identity Confusion in Multimodal Knowledge Editing](https://mlsanity.com/paper/2605.06096/) (2026)
- [ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems](https://mlsanity.com/paper/2503.20756/) (2025)
- [Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models](https://mlsanity.com/paper/2506.11253/) (2025)
- [CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs](https://mlsanity.com/paper/2605.06115/) (2026)
- [Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment](https://mlsanity.com/paper/2605.23780/) (2026)
- [Where a New Concept Must Enter: Entry Point Gates Cross-Task Usability in Unified Multimodal Models](https://mlsanity.com/paper/2608.17564/) (2026)

## Abstract (arXiv)

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks predominantly focus on coarse-grained knowledge, leaving the intricacies of fine-grained (FG) multimodal entity knowledge largely unexplored. This gap presents a notable challenge, as FG entity recognition is pivotal for the practical deployment and effectiveness of MLLMs in diverse real-world scenarios. To bridge this gap, we introduce MIKE, a comprehensive benchmark and dataset specifically designed for the FG multimodal entity knowledge editing. MIKE encompasses a suite of tasks tailored to assess different perspectives, including Vanilla Name Answering, Entity-Level Caption, and Complex-Scenario Recognition. In addition, a new form of knowledge editing, Multi-step Editing, is introduced to evaluate the editing efficiency. Through our extensive evaluations, we demonstrate that the current state-of-the-art methods face significant challenges in tackling our proposed benchmark, underscoring the complexity of FG knowledge editing in MLLMs. Our findings spotlight the urgent need for novel approaches in this domain, setting a clear agenda for future research and development efforts within the community.