  Fine-tuning large language models (LLMs) on multi-task instruction-following
data has been proven to be a powerful learning paradigm for improving their
zero-shot capabilities on new tasks. Recent works about high-quality
instruction-following data generation and selection require amounts of human
labor to conceive model-understandable instructions for the given tasks and
carefully filter the LLM-generated data. In this work, we introduce an
automatic instruction augmentation method named INSTRAUG in multimodal tasks.
It starts from a handful of basic and straightforward meta instructions but can
expand an instruction-following dataset by 30 times. Results on two popular
multimodal instructionfollowing benchmarks MULTIINSTRUCT and InstructBLIP show
that INSTRAUG can significantly improve the alignment of multimodal large
language models (MLLMs) across 12 multimodal tasks, which is even equivalent to
the benefits of scaling up training data multiple times.
