LongRecipe: Recipe for Efficient Long Context Generalization in Large
  Languge Models
Authors: name: Zhiyuan Hu, name: Yuliang Liu, name: Jinman Zhao, name: Suyuchen Wang, name: Yan Wang, name: Wei Shen, name: Qing Gu, name: Anh Tuan Luu, name: See-Kiong Ng, name: Zhiwei Jiang, name: Bryan Hooi

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 the
[link](https://github.com/zhiyuanhubj/LongRecipe).