Multimodal Benchmark

Multimodal benchmarks are standardized evaluation tools designed to assess the performance of multimodal large language models (MLLMs) across diverse tasks involving multiple data modalities (e.g., text, images, video, audio). Current research focuses on developing more comprehensive and efficient benchmarks that address issues like bias, redundancy, and the computational cost of evaluation, often incorporating human-level performance comparisons and exploring new evaluation metrics beyond simple accuracy. These benchmarks are crucial for advancing MLLM research by providing objective measures of model capabilities, facilitating fair comparisons between different architectures, and ultimately driving the development of more robust and reliable AI systems with broader real-world applications.