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This arXiv paper introduces CulturalBind, a 3.2K-image benchmark for testing whether vision-language models recognize culture-specific concepts and whether knowledge edits transfer across modalities. The key finding is that current editing methods fix individual visual errors but generalize poorly across modalities and degrade under sequential edits. For researchers and practitioners, this is a useful diagnostic of the limits of today's model-editing techniques for long-tail and underrepresented cultural knowledge.
Evaluating Cultural Knowledge Editing in Vision-Language Models
arXiv:2610.39602v1 Announce Type: cross Abstract: Current Vision-Language Models (VLMs) frequently fail to recognize culture-specific concepts, especially from underrepresented regions. Effectively injecting this long-tail knowledge into existing models remains a challenge. In this work, we investigate knowledge editing as a targeted alternative by introducing CulturalBind, a benchmark comprising 3.2K images of 1K concepts from 6 domains, designed to evaluate culture-aware cross-modal knowledge as well as concept-level knowledge editing in VLMs. Pre-edit evaluation reveals that textual and visual knowledge is decoupled: models often demonstrate text-based knowledge of cultural concepts but fail to identify them from images. We then evaluate editing methods in single and sequential scenarios. Although current methods successfully correct individual visual recognition errors, they exhibit low cross-modal portability, failing to integrate edits into the model's broader parametric knowledge. Furthermore, performance degrades rapidly under sequential editing, highlighting the need for comprehensive adaptation approaches that better consolidate multimodal knowledge over time.
Abstract:Current Vision-Language Models (VLMs) frequently fail to recognize culture-specific concepts, especially from underrepresented regions. Effectively injecting this long-tail knowledge into existing models remains a challenge. In this work, we investigate knowledge editing as a targeted alternative by introducing CulturalBind, a benchmark comprising 3.2K images of 1K concepts from 6 domains, designed to evaluate culture-aware cross-modal knowledge as well as concept-level knowledge editing in VLMs. Pre-edit evaluation reveals that textual and visual knowledge is decoupled: models often demonstrate text-based knowledge of cultural concepts but fail to identify them from images. We then evaluate editing methods in single and sequential scenarios. Although current methods successfully correct individual visual recognition errors, they exhibit low cross-modal portability, failing to integrate edits into the model's broader parametric knowledge. Furthermore, performance degrades rapidly under sequential editing, highlighting the need for comprehensive adaptation approaches that better consolidate multimodal knowledge over time.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2610.39602 [cs.CV]
(or arXiv:2610.39602v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.39602 Focus to learn more arXiv-issued DOI via DataCite
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