The study introduces LongAlign, a method for optimizing long context alignment in language models. It focuses on creating diverse long instruction data and fine-tuning models efficiently through packing, loss weighting, and sorted batching. LongAlign outperforms existing methods by up to 30% in long context tasks while maintaining proficiency in short tasks. [50 words]
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LongAlign: A Recipe for Long Context Alignment
Introduction
The study focuses on aligning long context by fine-tuning language models to interpret lengthy user prompts. Challenges include the absence of extensive datasets for supervised fine-tuning and difficulties in handling varied length distributions efficiently across multiple GPUs.
LongAlign Approach
Researchers from Tsinghua University and Zhipu.AI have developed LongAlign, a comprehensive approach for aligning LLMs to handle long contexts effectively. They construct a diverse, long instruction-following dataset using Self-Instruct, covering tasks from various sources. To address training inefficiencies due to varied length distributions, they employ packing and sorted batching strategies and a loss weighting method to balance contributions. They also introduce LongBench-Chat, an evaluation benchmark comprising open-ended questions of 10k-100k length.
Practical Solutions and Value
LongAlign offers practical solutions for effectively handling long contexts in LLMs. It involves constructing a diverse long instruction-following dataset using Self-Instruct, adopting efficient training strategies like packing and sorted batching, and introducing the LongBench-Chat benchmark for evaluation. Experiments demonstrate that LongAlign improves LLM performance on long-context tasks by up to 30% without compromising proficiency on shorter tasks. The open sourcing of LongAlign models, code, and data promotes further research and exploration in this field.
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