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.

翻译：多模态大语言模型通常分两阶段训练：首先在图像-文本对上进行预训练，随后利用有监督的视觉-语言指令数据进行微调。近期研究表明，即使使用有限数量的高质量指令遵循数据，大语言模型也能取得令人满意的结果。本文提出了InstructionGPT-4，该模型仅基于包含200个示例的小规模数据集进行微调，其数据量约为MiniGPT-4对齐数据集所用指令遵循数据的6%。为实现这一目标，我们首先提出了若干评估多模态指令数据质量的指标。基于这些指标，我们设计了一种可训练的有效数据选择器，用于自动识别并过滤低质量视觉-语言数据。采用该方法后，InstructionGPT-4在多项评估中均优于原始MiniGPT-4。总体而言，我们的研究结果表明，少量但高质量的指令调优数据能够高效提升多模态大语言模型的输出质量。我们的代码已开源至https://github.com/waltonfuture/InstructionGPT-4。