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This paper introduces PUMA, a new benchmark designed to evaluate how well multimodal AI models understand Polish language and culture across text, images, audio, and documents. The study reveals significant gaps in current frontier models, particularly regarding complex audio processing and document understanding outside the English domain. By open-sourcing this framework, the authors aim to drive more inclusive research into localized multimodal capabilities.
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PUMA: A Polish Benchmark for Culturally Grounded Multimodal Understanding

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.21853v1 Announce Type: new Abstract: Large language models are increasingly moving beyond text processing, adding support for other modalities such as images and audio. While text understanding and generation have been extensively studied, multimodal data processing capabilities, particularly in the context of cultures and languages other than English, have not yet been evaluated comprehensively. In this paper, we propose PUMA (Polish Unified Multimodal Assessment), a novel benchmark of 900 hand-crafted tasks designed to probe the limits of multimodal models in the Polish cultural and linguistic context. The dataset evaluates both cultural understanding and practical skill in processing text, images, audio, and visually rich documents. Our extensive evaluation of frontier commercial models, open-weights models, and specialized smaller systems highlights a significant performance gap. While top commercial models achieve high scores in visual question answering, most models struggle with complex audio or document understanding. We open-source our evaluation framework to advance localized multimodal AI research.

Abstract:Large language models are increasingly moving beyond text processing, adding support for other modalities such as images and audio. While text understanding and generation have been extensively studied, multimodal data processing capabilities, particularly in the context of cultures and languages other than English, have not yet been evaluated comprehensively. In this paper, we propose PUMA (Polish Unified Multimodal Assessment), a novel benchmark of 900 hand-crafted tasks designed to probe the limits of multimodal models in the Polish cultural and linguistic context. The dataset evaluates both cultural understanding and practical skill in processing text, images, audio, and visually rich documents. Our extensive evaluation of frontier commercial models, open-weights models, and specialized smaller systems highlights a significant performance gap. While top commercial models achieve high scores in visual question answering, most models struggle with complex audio or document understanding. We open-source our evaluation framework to advance localized multimodal AI research.

Computation and Language (cs.CL)

arXiv:2608.21853 [cs.CL]

(or arXiv:2608.21853v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.21853 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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