Long Range Language

Long-range language modeling aims to enable language models to process and understand significantly longer text sequences than traditional architectures allow, improving performance on tasks requiring extensive contextual information. Current research focuses on developing efficient attention mechanisms, such as linear attention and sparse attention, and incorporating external memory systems like vector caches or retrieval modules to handle the increased computational demands of longer contexts. These advancements are crucial for improving the performance of language models on complex tasks involving extensive discourse and improving the understanding of long-range dependencies in text, with applications ranging from code completion to question answering.