  Recent advances in large language models elicit reasoning in a chain of
thought that allows models to decompose problems in a human-like fashion.
Though this paradigm improves multi-step reasoning ability in language models,
it is limited by being unimodal and applied mainly to question-answering tasks.
We claim that incorporating visual augmentation into reasoning is essential,
especially for complex, imaginative tasks. Consequently, we introduce VCoT, a
novel method that leverages chain of thought prompting with vision-language
grounding to recursively bridge the logical gaps within sequential data. Our
method uses visual guidance to generate synthetic multimodal infillings that
add consistent and novel information to reduce the logical gaps for downstream
tasks that can benefit from temporal reasoning, as well as provide
interpretability into models' multi-step reasoning. We apply VCoT to the Visual
Storytelling and WikiHow summarization datasets and demonstrate through human
evaluation that VCoT offers novel and consistent synthetic data augmentation
beating chain of thought baselines, which can be used to enhance downstream
performance.

Computation and Language;Computer Vision and Pattern Recognition