  Human language is grounded on multimodal knowledge including visual knowledge
like colors, sizes, and shapes. However, current large-scale pre-trained
language models rely on text-only self-supervised training with massive text
data, which precludes them from utilizing relevant visual information when
necessary. To address this, we propose a novel pre-training framework, named
VaLM, to Visually-augment text tokens with retrieved relevant images for
Language Modeling. Specifically, VaLM builds on a novel latent text-image
alignment method via an image retrieval module to fetch corresponding images
given a textual context. With the visually-augmented context, VaLM uses a
visual knowledge fusion layer to enable multimodal grounded language modeling
by attending to both text context and visual knowledge in images. We evaluate
VaLM on various visual knowledge-intensive commonsense reasoning tasks, which
require visual information to excel. The experimental results illustrate that
VaLM outperforms all strong language-only and vision-language baselines with
substantial gains in reasoning object commonsense including color, size, and
shape. Our code is available at https://github.com/Victorwz/VaLM.
