Hierarchical Context

Hierarchical context modeling aims to improve the processing and understanding of information by leveraging relationships across multiple scales or levels of granularity. Current research focuses on developing methods to efficiently handle long sequences of data, such as in natural language processing and speech synthesis, often employing hierarchical architectures and algorithms like ADMM or contrastive learning to capture both local and global context. These advancements are significant for improving the performance of large language models, enhancing speech synthesis, and enabling more robust applications in areas like image translation and semantic segmentation.