Cross Modal Reasoning

Cross-modal reasoning (CMR) focuses on enabling artificial intelligence systems to understand and reason across different data modalities, such as text, images, and video, mirroring human cognitive abilities. Current research heavily utilizes large language models (LLMs) and multimodal large language models (MLLMs), often incorporating techniques like chain-of-thought prompting and attention mechanisms to improve cross-modal interaction and reasoning capabilities. This field is crucial for advancing AI towards more general intelligence, with applications ranging from improved visual question answering and fake news detection to more sophisticated robotics and multimodal information retrieval. Benchmark datasets and evaluation metrics are actively being developed to drive progress and facilitate fair comparisons between different approaches.