Longrecipe: Recipe For Efficient Long Context Generalization In Large Language Models

Hu Zhiyuan, Liu Yuliang, Zhao Jinman, Wang Suyuchen, Wang Yan, Shen Wei, Gu Qing, Luu Anh Tuan, Ng See-kiong, Jiang Zhiwei, Hooi Bryan. Arxiv 2024

[Paper] [Code]    
Efficiency And Optimization GPT Has Code Model Architecture Pretraining Methods Training Techniques

Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. Meanwhile, extending the context window in LLMs through post-pretraining is highly resource-intensive. To address this, we introduce LongRecipe, an efficient training strategy for extending the context window of LLMs, including impactful token analysis, position index transformation, and training optimization strategies. It simulates long-sequence inputs while maintaining training efficiency and significantly improves the model’s understanding of long-range dependencies. Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size, and reduces computational training resource over 85% compared to full sequence training. Furthermore, LongRecipe also preserves the original LLM’s capabilities in general tasks. Ultimately, we can extend the effective context window of open-source LLMs from 8k to 128k, achieving performance close to GPT-4 with just one day of dedicated training using a single GPU with 80G memory. Our code is released at https://github.com/zhiyuanhubj/LongRecipe.

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