  Multimodal large language models are typically trained in two stages: first
pre-training on image-text pairs, and then fine-tuning using supervised
vision-language instruction data. Recent studies have shown that large language
models can achieve satisfactory results even with a limited amount of
high-quality instruction-following data. In this paper, we introduce
InstructionGPT-4, which is fine-tuned on a small dataset comprising only 200
examples, amounting to approximately 6\% of the instruction-following data used
in the alignment dataset for MiniGPT-4. To achieve this, we first propose
several metrics to access the quality of multimodal instruction data. Based on
these metrics, we present an effective and trainable data selector to
automatically identify and filter low-quality vision-language data. By
employing this method, InstructionGPT-4 outperforms the original MiniGPT-4 on
various evaluations. Overall, our findings demonstrate that less but
high-quality instruction tuning data is efficient in enabling multimodal large
language models to generate better output. Our code is available at
https://github.com/waltonfuture/InstructionGPT-4.
