Modal Translation

Modal translation focuses on translating information across different modalities, such as text, speech, images, and even physiological signals like EEG and MRI data. Current research emphasizes leveraging large language models (LLMs) and transformer architectures to achieve this cross-modal transfer, often employing techniques like instruction-tuning and cross-modal attention mechanisms. This field is significant for advancing machine translation capabilities beyond text alone, with applications ranging from improved accessibility for low-resource languages to enhanced medical diagnosis through the integration of diverse data sources.