Long Context Alignment with Short Instructions and Synthesized Positions
Abstract: Effectively handling instructions with extremely long context remains a challenge for Large Language Models (LLMs), typically necessitating high-quality long data and substantial computational resources.
This paper introduces Step-Skipping Alignment (SkipAlign), a new technique designed to enhance the long-context capabilities of LLMs in the phase of alignment without the need for additional efforts beyond training with original data length.
SkipAlign is developed on the premise that long-range dependencies are fundamental to enhancing an LLM's capacity of long context.
Departing from merely expanding the length of input samples, SkipAlign synthesizes long-range dependencies from the aspect of positions indices.
This is achieved by the strategic insertion of skipped positions within instruction-following samples, which utilizes the semantic structure of the data to effectively expand the context.
Through extensive experiments on base models with a variety of context window sizes, SkipAlign demonstrates its effectiveness across a spectrum of long-context tasks.
Particularly noteworthy is that with a careful selection of the base model and alignment datasets, SkipAlign with only 6B parameters achieves it's best performance and comparable with strong baselines like GPT-3.5-Turbo-16K on LongBench. The code and SkipAligned models will be open-sourced.
Paper Type: Long
Research Area: Generation
Research Area Keywords: long context
Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 4244
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