MultimodalQA Dataset

MultiModalQA datasets are designed to benchmark question answering systems' ability to reason across diverse data modalities, such as text, images, and tables. Current research focuses on developing models that effectively integrate information from these sources, employing techniques like program-based prompting, large language model-based fusion strategies, and multimodal graph transformers to improve accuracy and efficiency. These advancements are significant because they push the boundaries of artificial intelligence towards more human-like reasoning capabilities and have implications for applications requiring complex information retrieval and synthesis from heterogeneous sources.