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 cross-lingual consistency. This underscores the need for more robust approaches that produce truly multilingual and culturally aware models.
@article{arxiv.2505.15075,
title = {Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs},
author = {Hao Wang and Pinzhi Huang and Jihan Yang and Saining Xie and Daisuke Kawahara},
journal= {arXiv preprint arXiv:2505.15075},
year = {2025}
}
Comments
The first version of this paper mistakenly included a prompt injection phrase, which was inappropriate and unprofessional. Although we corrected the version on arXiv and withdrew from the conference, my co-authors and university strongly request a full withdrawal. Given the situation, I no longer have the authority to manage this paper, and withdrawing it from arXiv is the most responsible action