PISA-Bench: The PISA Index as a Multilingual and Multimodal Metric for the Evaluation of Vision-Language Models

• URL: http://arxiv.org/abs/2510.24792v1
• Date: Mon, 27 Oct 2025 11:00:45 GMT
• Title: PISA-Bench: The PISA Index as a Multilingual and Multimodal Metric for the Evaluation of Vision-Language Models
• Authors: Patrick Haller, Fabio Barth, Jonas Golde, Georg Rehm, Alan Akbik,
• Abstract summary: We introduce PISA-Bench, a benchmark derived from English examples of the expert-created PISA tests.<n>Each example consists of human-extracted instructions, questions, answer options, and images, enriched with question type categories.<n>We evaluate state-of-the-art vision-language models on PISA-Bench and find that especially small models (20B parameters) fail to achieve high test scores.
• Score: 13.316431293058763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have demonstrated remarkable progress in multimodal reasoning. However, existing benchmarks remain limited in terms of high-quality, human-verified examples. Many current datasets rely on synthetically generated content by large language models (LLMs). Furthermore, most datasets are limited to English, as manual quality assurance of translated samples is time-consuming and costly. To fill this gap, we introduce PISA-Bench, a multilingual benchmark derived from English examples of the expert-created PISA tests, a unified framework for the assessment of student competencies in over eighty countries. Each example consists of human-extracted instructions, questions, answer options, and images, enriched with question type categories, and has been translated from English into five additional languages (Spanish, German, Chinese, French, and Italian), resulting in a fully parallel corpus covering six languages. We evaluate state-of-the-art vision-language models on PISA-Bench and find that especially small models (<20B parameters) fail to achieve high test scores. We further find substantial performance degradation on non-English splits as well as high error-rates when models are tasked with spatial and geometric reasoning. By releasing the dataset and evaluation framework, we provide a resource for advancing research on multilingual multimodal reasoning.

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