Context Compression

Context compression aims to reduce the computational and memory burden of processing long sequences in large language models (LLMs) without significant performance loss. Current research focuses on developing efficient compression algorithms, often leveraging attention mechanisms or convolutional networks, to create compact representations of lengthy contexts, including both textual and multimodal data. These techniques are crucial for improving the efficiency and scalability of LLMs in applications like retrieval-augmented generation and question answering, enabling the processing of longer documents and more complex tasks. The resulting advancements have significant implications for deploying LLMs in resource-constrained environments and for enhancing their performance on knowledge-intensive tasks.