arXiv cs.AI· Zhiwu Wang, Yichen Wu, Jinqian Yang, Yizhou Yang, Renzhen Wang, Qian Zhao, Deyu Meng·· 3 小时前AI 评分33
多模态知识编辑中的证据误 grounding 诊断与缓解：WaterBird-Edit 与 VICE 方法
Diagnosing and Mitigating Evidence Misgrounding in Multimodal Knowledge Editing
AI 导读
arXiv 论文提出 WaterBird-Edit 分析集，诊断多模态大语言模型（MLLM）知识编辑中的证据误 grounding：编辑后的答案可能只对背景上下文敏感而非目标视觉证据。
正文 · 原文
Abstract:Multimodal knowledge editing aims to update visually grounded facts in Multimodal Large Language Models (MLLMs) while preserving unrelated text and visual behaviors. However, standard answer-level metrics can hide evidence misgrounding: an edit may produce the target answer on the original image yet remain sensitive to incidental context rather than the intended visual evidence. To examine this issue in a controlled setting, we construct WaterBird-Edit, an analysis set that decouples bird habitat labels from background context and measures whether edited predictions remain stable when the target evidence is preserved but the background changes. Motivated by this diagnosis, we propose VICE (Visual In-Context Editing), a retrieval-based framework that stores edits in external memory and applies them through visual in-context demonstrations designed to expose task-relevant evidence. Experiments on three standard benchmarks show that VICE achieves strong lifelong editing performance, while results on WaterBird-Edit show that it substantially reduces sensitivity to the controlled background shifts. Code and dataset are available in this https URL.
| Comments: | Accepted to Findings of EMNLP 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33075 [cs.CV] |
| (or arXiv:2610.33075v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33075 arXiv-issued DOI via DataCite |
Submission history
From: Zhiwu Wang [view email]
[v1]
Wed, 21 Oct 2026 07:00:01 UTC (605 KB)
来源：arXiv cs.AI · arxiv.org