Not Truly Multilingual: Script Consistency as a Missing Dimension in VLM Evaluation

Abstract:Current multilingual evaluations for Vision-Language Models (VLMs) assume a one-to-one mapping between language and orthography, overlooking billions of users of multi-script languages. We introduce PuMVR (Punjabi Multimodal Visual Reasoning), a benchmark of 1,000 strictly parallel image-text instances across Punjabi's three active scripts: Gurmukhi, Shahmukhi, and Roman. Evaluating 10 state-of-the-art VLMs, we expose a substantial and systematic Script Gap. Models frequently solve visual tasks in one script while failing identical tasks in another, with accuracy deltas reaching 16%. Crucially, visual input boosts absolute performance uniformly yet does not close the orthographic gap. Furthermore, cross-script in-context transfer is highly brittle, exposing script-locked knowledge representation. Supported by McNemar tests across all script pairs, our findings demonstrate that current "multilingual" VLMs are not truly multi-script. We propose the Script Consistency Rate (SCR), which falls as low as 24.8% on our benchmark, as a mandatory metric for script-agnostic evaluation to ensure equitable AI access. Data and code are available at: this https URL.

Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)

arXiv:2606.17188 [cs.CV]

(or arXiv:2606.17188v3 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.17188 Focus to learn more arXiv-issued DOI via DataCite

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