The Sequence Knowledge #545 : Beyond Language, Learning About Multimodal Benchmarks
Some of the most important evals for computer vision, audio and more.
Today we will Discuss:
An introduction to multimodal benchmarks.
A review of Tencent’s SEED-Bench benchmark for vision-language models.
💡 AI Concept of the Day: An Overview of Multimodal Benchmarks
Evaluating multimodal AI systems introduces unique challenges that go beyond traditional language benchmarks. These models must perform well across multiple input types—including text, images, audio, and video—while reasoning coherently across these modalities. This complexity requires specialized benchmark suites that can assess a model's ability to perceive, reason, and generate content across diverse information channels.
MMMU (Multimodal Machine Understanding) is a foundational benchmark in this space. It contains 11,500 college-level questions across STEM, humanities, social sciences, and business domains. What sets MMMU apart is its requirement for models to process text and visuals together, solving problems that often involve mathematical reasoning, interpreting diagrams, or analyzing domain-specific visual data. The benchmark enforces strict input-output constraints to reduce reliance on shortcuts and promote genuine comprehension. Its layered difficulty and subject stratification allow for detailed performance analysis and insights into the strengths and weaknesses of current architectures.