VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark

arXiv:2508.13680 · cs.CL, cs.LG · Submitted 2025-08-19 · Read on arXiv

cs.CL, cs.LG

Submitted: 2025-08-19

Updated: 2026-09-23

Terminology

• Accuracy — 43× in this paper · explained in 1 episode
• Benchmark — 40× in this paper · explained in 2 episodes
• Grounding — 20× in this paper · explained in 3 episodes
• Multimodal Reasoning — 15× in this paper · explained in 5 episodes
• Verification — 8× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Multimodal Integration — 5× in this paper · explained in 2 episodes
• Visual Grounding — 5× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Ministry of Education and Training — 3× in this paper
• Ministry of Transportation — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models
• Qwen2.5-VL Technical Report
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models
• Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
• Gemma: Open Models Based on Gemini Research and Technology
• JMMMU-Pro: Image-based Japanese Multi-discipline Multimodal Understanding Benchmark via Vibe Benchmark Construction
• CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
• Vision Language Models are Biased

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