Introduction to SEED-Bench: Benchmarking Multimodal Large Language Models
SEED-Bench is an innovative and comprehensive benchmarking tool designed to evaluate multimodal large language models. It serves as a critical resource for developers and researchers in the field of artificial intelligence and machine learning, specifically those working on language and vision models.
What is SEED-Bench?
SEED-Bench is a series of benchmarks that assess various dimensions of large language models capable of processing and generating both text and images. The benchmarks aim to measure performance across a wide array of tasks and scenarios that mimic real-world applications.
The Different Versions of SEED-Bench
The SEED-Bench project is presented in different versions, each expanding on the previous one with more comprehensive evaluation dimensions:
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SEED-Bench-1: This initial version features 19,000 multiple-choice questions with human annotations across 12 dimensions, focusing on tasks that require spatial and temporal understanding.
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SEED-Bench-2: Building on the first, this version includes 24,000 questions across 27 dimensions, covering a broader range of tasks pertinent to both text and image generation.
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SEED-Bench-2-Plus: Designed specifically for text-rich visual comprehension, it comprises 2,300 questions and emphasizes categories like Charts, Maps, and Webs.
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SEED-Bench-H: This is a holistic integration of the previous versions with additional evaluation areas, featuring a total of 28,000 questions spanning 34 dimensions.
Key Features and Uses
SEED-Bench aims to provide a rigorous evaluation framework for models that tackle tasks involving understanding, generating, and interacting with both text and image data. This is achieved through:
- Human Annotations: Each question is carefully annotated by humans to ensure precision in testing and evaluation.
- Multidimensional Evaluation: Models are assessed on multiple fronts, including but not limited to text recognition, emotion recognition, visual mathematics, text-to-image generation, and more.
- Real-World Scenarios: The questions cover real-world applications and scenarios, helping to gauge a model's practical applicability.
Current Developments
The SEED-Bench project is continuously being developed and improved. Some noteworthy updates include:
- The release of SEED-Bench-H and its accompanying dataset on ModelScope.
- The incorporation of new evaluation dimensions such as Image to LaTeX conversion and Few-shot learning tasks.
- The ongoing evaluation of notable AI models like GPT-4v and Gemini-Vision-Pro using SEED-Bench-1 and SEED-Bench-2.
Conclusion
SEED-Bench is a pivotal benchmarking tool for the AI community, offering detailed insights into the capabilities and limitations of multimodal large language models. With its multidimensional approach and large-scale question sets, it provides a structured and comprehensive evaluation environment for testing cutting-edge AI technologies.