MBZUAI · Significant research
MBZUAI researchers presented EXAMS-V, a new benchmark dataset for evaluating the reasoning and processing abilities of vision language models (VLMs). EXAMS-V contains over 20,000 multiple-choice questions across 26 subjects and 11 languages, including Arabic. The dataset presents the questions within images, testing the VLM's ability to integrate visual and textual information. Why it matters: This dataset fills a gap in VLM evaluation, providing a valuable resource for assessing and improving the multimodal reasoning capabilities of these models, particularly in diverse languages like Arabic.
VLM · benchmark dataset · MBZUAI · EXAMS-V · multimodal
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arXiv ·
A new benchmark, ViMUL-Bench, is introduced to evaluate video LLMs across 14 languages, including Arabic, with a focus on cultural inclusivity. The benchmark includes 8k manually verified samples across 15 categories and varying video durations. A multilingual video LLM, ViMUL, is also presented, along with a training set of 1.2 million samples, with both to be publicly released.
arXiv ·
A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.