Jako Tako or Fluent? Presenting PoVisLE: A Polish Vision-Language Evaluation
arXiv:2608.07763v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance on tasks such as image captioning, visual question answering, and image-to-text generation. However, they are predominantly trained on English-centric data, which limits their ability to handle culturally grounded visual understanding and leads to failures in interpreting region-specific meanings, symbolic content, and context-dependent visual cues. Existing benchmarks for cultural competence are often template-driven and focused on surface-level recognition, making them insufficient for evaluating deeper linguistic and pragmatic understanding in culturally situated settings. We introduce PoVisLE, a monocultural vision-language benchmark for Polish designed to evaluate culturally grounded multimodal understanding under a grounded evaluation paradigm, where language is interpreted in interaction with visual context. The dataset contains 1,117 images and 2,366 manually annotated VQA pairs. Overall, our dataset provides a controlled and challenging resource for assessing culturally grounded vision-language understanding beyond surface-level recognition.
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[Submitted on 7 Aug 2026]
Title:Jako Tako or Fluent? Presenting PoVisLE: A Polish Vision-Language Evaluation
View a PDF of the paper titled Jako Tako or Fluent? Presenting PoVisLE: A Polish Vision-Language Evaluation, by Anna Ko{\l}os and 5 other authors
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Abstract:Vision-language models (VLMs) have achieved strong performance on tasks such as image captioning, visual question answering, and image-to-text generation. However, they are predominantly trained on English-centric data, which limits their ability to handle culturally grounded visual understanding and leads to failures in interpreting region-specific meanings, symbolic content, and context-dependent visual cues. Existing benchmarks for cultural competence are often template-driven and focused on surface-level recognition, making them insufficient for evaluating deeper linguistic and pragmatic understanding in culturally situated settings. We introduce PoVisLE, a monocultural vision-language benchmark for Polish designed to evaluate culturally grounded multimodal understanding under a grounded evaluation paradigm, where language is interpreted in interaction with visual context. The dataset contains 1,117 images and 2,366 manually annotated VQA pairs. Overall, our dataset provides a controlled and challenging resource for assessing culturally grounded vision-language understanding beyond surface-level recognition.
Comments: 28 pages. Preprint under review
Subjects:
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.07763 [cs.CL]
(or arXiv:2608.07763v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.07763
arXiv-issued DOI via DataCite (pending registration)
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From: Wojciech Kusa [view email] [v1] Fri, 7 Aug 2026 21:01:14 UTC (11,726 KB)
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