Structured Packing in LLM Training Improves Long Context Utilization
This work addresses a practical limitation in long-context LLMs for applications requiring efficient context use, representing an incremental improvement through a novel training data structuring method.
The paper tackles the problem of suboptimal context utilization in long-context large language models by introducing the Structured Packing for Long Context (SPLiCe) method, which structures training data to enhance semantic interdependence, achieving improved performance on tasks like Qasper and HotpotQA across models of 3B, 7B, and 13B parameters and mitigating the lost-in-middle phenomenon.
Recent advancements in long-context large language models have attracted significant attention, yet their practical applications often suffer from suboptimal context utilization. This study investigates structuring training data to enhance semantic interdependence, demonstrating that this approach effectively improves context utilization. To this end, we introduce the Structured Packing for Long Context (SPLiCe) method, which utilizes retrieval to collate mutually relevant documents into long and coherent training examples. We validate SPLiCe empirically across models of varying sizes -- 3B, 7B, and 13B -- achieving improved performance in long-context tasks, such as Qasper and HotpotQA. Remarkably, even brief fine-tuning with SPLiCe is sufficient to realize these benefits. Additionally, SPLiCe effectively mitigates the lost-in-middle phenomenon often observed in large models. Our comprehensive analysis of SPLiCe explores its design choices and reveals intriguing transfer effects; for instance, training on programming code enhances performance on natural language tasks.