Context Pruning

Context pruning is a technique used to optimize large language models (LLMs) by selectively removing less important information from the input context, thereby improving efficiency and performance. Current research focuses on developing algorithms that intelligently identify and prune irrelevant information, employing methods like hierarchical pruning for code completion, contrastive learning for debiasing, and reinforcement learning for enhanced reasoning. These advancements aim to reduce computational costs and memory requirements of LLMs while maintaining or even improving accuracy on various downstream tasks, leading to more efficient and resource-friendly AI systems.