Analyzing the Multilingual Ability of Vision-Language Models to Generate Explanations for Artworks

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Key Points

The aim is to evaluate the multilingual ability of vision-language models (VLMs) in generating explanations for artworks and assess the impact of English instruction on other languages.
Proposed a multilingual image understanding dataset without relying on machine translation.
Conducted experiments with various models to evaluate the explanation generation abilities of VLMs.
Investigated whether English training improves performance in other languages.
VLMs demonstrated decreased performance when tasked with generating explanations in languages other than English.
Identified challenges in effectively leveraging knowledge from English data for other languages.
Highlighting the necessity for multilingual training in both image encoder learning and integrated learning processes.

Assesses explanation generation ability in multiple languages using a novel dataset, indicating challenges in VLM performance.

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Ozaki et al. (2026) studied this question.

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