Long Sequence Processing

Long sequence processing aims to enable large language models (LLMs) to effectively handle significantly longer input sequences than currently possible, addressing the quadratic complexity of standard attention mechanisms. Current research focuses on developing efficient algorithms and architectures, such as sparse attention, chunking methods, and modified transformer designs (e.g., BigBird, Mamba), to reduce computational costs and memory requirements while maintaining accuracy. These advancements are crucial for enabling LLMs to process complex data like complete medical records or extensive genomic sequences, significantly impacting fields like healthcare and computational biology.