LongAlign: A Recipe for Long Context Alignment of Large Language Models

• URL: http://arxiv.org/abs/2401.18058v1
• Date: Wed, 31 Jan 2024 18:29:39 GMT
• Title: LongAlign: A Recipe for Long Context Alignment of Large Language Models
• Authors: Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, Juanzi Li
• Abstract summary: LongAlign is a recipe of the instruction data, training, and evaluation for long context alignment. We construct a long instruction-following dataset using Self-Instruct. We adopt the packing and sorted strategies to speed up supervised fine-tuning on data with varied length distributions.
• Score: 61.85923382850057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extending large language models to effectively handle long contexts requires instruction fine-tuning on input sequences of similar length. To address this, we present LongAlign -- a recipe of the instruction data, training, and evaluation for long context alignment. First, we construct a long instruction-following dataset using Self-Instruct. To ensure the data diversity, it covers a broad range of tasks from various long context sources. Second, we adopt the packing and sorted batching strategies to speed up supervised fine-tuning on data with varied length distributions. Additionally, we develop a loss weighting method to balance the contribution to the loss across different sequences during packing training. Third, we introduce the LongBench-Chat benchmark for evaluating instruction-following capabilities on queries of 10k-100k in length. Experiments show that LongAlign outperforms existing recipes for LLMs in long context tasks by up to 30\%, while also maintaining their proficiency in handling short, generic tasks. The code, data, and long-aligned models are open-sourced at https://github.com/THUDM/LongAlign.

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