Structured Packing in LLM Training Improves Long Context Utilization

Recent advances in long-context Large Language Models (LCLMs) have generated significant interest, especially in applications such as querying scientific research papers. However, their potential is often limited by inadequate context utilization. We identify the absence of long-range semantic dependencies in typical training data as a primary hindrance. To address this, we delve into the benefits of frequently incorporating related documents into training inputs. Using the inherent directory structure of code data as a source of training examples, we demonstrate improvements in perplexity, even for tasks unrelated to coding. Building on these findings, but with a broader focus, we introduce Structured Packing for Long Context (SPLiCe). SPLiCe is an innovative method for creating training examples by using a retrieval method to collate the most mutually relevant documents into a single training context. Our results indicate that \method{} enhances model performance and can be used to train large models to utilize long contexts better. We validate our results by training a large $3$B model, showing both perplexity improvements and better long-context performance on downstream tasks.