Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs, but lacks an in-depth study of the visual language pre-training process, where the model learns to perform joint modeling on both modalities. In this work, we examine the design options for VLM pre-training by augmenting LLM towards VLM through step-by-step controllable comparisons. We introduce three main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance, but lack in-context learning capability, which requires unfreezing the LLM; (2) interleaved pre-training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks, but also boosts VLM task accuracy. With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, e.g., LLaVA-1.5, across main benchmarks without bells and whistles. Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.

翻译：视觉语言模型（VLM）随着大语言模型的最新成功而迅速发展。尽管已有大量研究致力于通过视觉指令微调将大语言模型扩展至视觉输入领域，但对于视觉语言预训练过程——即模型学习对两种模态进行联合建模的过程——缺乏深入探究。本研究通过逐步可控比较的方式，将大语言模型增强为视觉语言模型，系统考察了视觉语言模型预训练的设计选项。我们提出三项主要发现：（1）预训练期间冻结大语言模型可获得尚可的零样本性能，但缺乏上下文学习能力，需要解冻大语言模型；（2）交错式预训练数据具有优势，而仅使用图像-文本对并非最优方案；（3）在指令微调阶段将纯文本指令数据与图像-文本数据重新混合，不仅能弥补纯文本任务性能的下降，还可提升视觉语言模型的任务准确率。基于改进的预训练方案，我们构建了VILA视觉语言模型系列，该系列在不依赖复杂技巧的情况下，在主要基准测试中持续优于现有最优模型（如LLaVA-1.5）。多模态预训练还揭示了VILA的若干诱人特性，包括多图像推理能力、增强的上下文学习能力以及更优的世界知识。