Multimodal Corpus

Multimodal corpora are collections of data integrating various modalities like text, audio, images, and video, aiming to better understand and model human communication. Current research focuses on developing large-scale, multilingual multimodal corpora and leveraging them to train and evaluate multimodal large language models (mLLMs), often employing techniques like image-text interleaving and schema-based approaches for improved in-context learning and task performance. These corpora are crucial for advancing natural language processing, particularly in areas like emotion recognition, video editing, and cross-lingual understanding, enabling the development of more robust and human-like AI systems.