10 Newest Ways for Efficient Processing of Long Context in LLMs

Handling long context remains a challenging issue for LLMs and other AI systems. That’s why more and more approaches for optimizing LLMs' processing of long context are appearing.

Most new approaches focus on breaking down long texts into smaller, manageable parts. However, this may lead to losing some parts of the context or the text's core idea. This process may also take much time, so developers aim to include parallel processing of multiple parts in their methods, which usually requires more computational power. Because this approach is not always effective, researchers are developing other techniques for working with long context windows.

Here are 10 novel methods for efficient processing of long contexts in LLMs: