Long Context

Long context in large language models (LLMs) focuses on enhancing the ability of these models to process and reason over significantly extended input sequences, exceeding the limitations of traditional context windows. Current research emphasizes developing novel attention mechanisms (e.g., sparse attention, differential attention) and efficient memory management techniques (e.g., compression, retrieval-augmentation) to overcome computational and memory bottlenecks associated with longer contexts. This area is crucial for advancing LLMs' capabilities in complex tasks requiring holistic understanding of extensive information, such as question answering, summarization, and multi-modal reasoning, impacting both scientific understanding of LLMs and their practical applications.