Multilingual Multimodal Benchmark

Multilingual multimodal benchmarks are datasets designed to evaluate the ability of artificial intelligence models to understand and process information from multiple languages and modalities (e.g., text, images, audio). Current research focuses on developing these benchmarks to address limitations in existing datasets, particularly the lack of diversity in language and the need for more complex, nuanced tasks that go beyond simple image-captioning. These benchmarks are crucial for advancing the development of robust and inclusive multimodal models, with applications ranging from improved machine translation and visual question answering to more effective analysis of diverse media content. The creation of these benchmarks is driving progress in model architectures, such as those incorporating Mixture-of-Experts and leveraging large language models for data augmentation and improved multilingual capabilities.