Year: 2026
Type: conference-paper
Abstract: The rapid evolution of multimodal large language models (MLLMs) has significantly enhanced their real-world applications.However, achieving consistent performance across languages, especially when integrating cultural knowledge, remains a significant challenge.To better assess this issue, we introduce two new benchmarks: KnowRecall and VisRecall, which evaluate cross-lingual consistency in MLLMs.KnowRecall is a visual question answering benchmark designed to measure factual knowledge consistency in 15 languages, focusing on cultural and historical questions about global landmarks.VisRecall assesses visual memory consistency by asking models to describe landmark appearances in 9 languages without access to images.Experimental results reveal that state-of-the-art MLLMs, including proprietary ones, still struggle to achieve crosslingual consistency.This underscores the need for more robust approaches that produce truly multilingual and culturally aware models.
Authors: Hao Wang, Pinzhi Huang, Daisuke Kawahara
Institutions: Waseda University, New York University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Waseda University
OpenAlex ID: W7166841967