Multi Modal Benchmark

Multi-modal benchmarks are crucial for evaluating the performance of models that process and integrate information from multiple data types (e.g., text, images, audio). Current research focuses on developing comprehensive benchmarks that address limitations in existing datasets, such as insufficient diversity, lack of long-context understanding, and potential data leakage, often employing large language models (LLMs) for data generation and annotation. These benchmarks are vital for advancing the development of robust multi-modal models and improving applications across diverse fields, including video understanding, document analysis, and e-commerce.