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arXiv · 2508.21294

BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning

João Guilherme Alves Santos·Giovana Kerche Bonás·Thales Sales Almeida
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Abstract

With the growing capabilities of Large Language Models (LLMs), there is an increasing need for robust evaluation methods, especially in multilingual and non-English contexts. We present an updated version of the BLUEX dataset, now including 2024-2025 exams and automatically generated image captions using state-of-the-art models, enhancing its relevance for data contamination studies in LLM pretraining. Captioning strategies increase accessibility to text-only models by more than 40%, producing 1,422 usable questions, more than doubling the number in the original BLUEX. We evaluated commercial and open-source LLMs and their ability to leverage visual context through captions.

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João Guilherme Alves Santos, Giovana Kerche Bonás, Thales Sales Almeida. 2025-08-29. BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning. https://arxiv.org/abs/2508.21294

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