Seed-bench: Benchmarking multimodal large language models

Analysis

Why This Paper Matters

Multimodal Large Language Models (MLLMs) are rapidly advancing, but their evaluation remains fragmented. SEED-Bench addresses this by providing a comprehensive, large-scale benchmark that systematically assesses hierarchical capabilities—from basic perception to complex reasoning. This matters because without standardized evaluation, progress in MLLMs is difficult to measure and compare. The benchmark fills a critical gap in the AI ecosystem, enabling researchers to identify specific strengths and weaknesses of their models.

Technical Contributions

• Large-scale benchmark: SEED-Bench includes a diverse set of multimodal tasks, covering multiple levels of cognitive complexity.
• Hierarchical evaluation: The benchmark is structured to assess capabilities in a layered manner, from low-level perception to high-level reasoning.
• Standardized protocol: Provides a consistent evaluation framework that facilitates fair comparisons across different MLLMs.

Results

The paper reports that current MLLMs show significant performance variation across capability levels. Models excel at basic perception tasks but struggle with complex reasoning that requires integrating multiple modalities. The benchmark reveals specific failure modes, such as difficulties with temporal reasoning and cross-modal grounding.

Significance

SEED-Bench sets a new standard for MLLM evaluation, similar to how GLUE and SuperGLUE advanced NLP. It will likely drive improvements in multimodal architectures by highlighting areas needing innovation. The benchmark's hierarchical design also encourages development of models with more robust reasoning abilities, moving beyond simple pattern matching.