Bidirectional Transformer

Bidirectional transformers process sequential data by considering both preceding and succeeding elements simultaneously, unlike unidirectional models. Current research focuses on improving efficiency and addressing limitations like computational cost and handling long sequences, leading to novel architectures such as selective transformers and memory-efficient bidirectional transformers. These advancements are impacting diverse fields, enhancing performance in tasks ranging from image generation and semantic segmentation to vulnerability detection in code and medical document analysis. The ability to efficiently process and understand context in complex data is driving significant progress across numerous applications.