  When presented with questions involving visual thinking, humans naturally
switch reasoning modalities, often forming mental images or drawing visual
aids. Large language models have shown promising results in arithmetic and
symbolic reasoning by expressing intermediate reasoning in text as a chain of
thought, yet struggle to extend this capability to answer text queries that are
easily solved by visual reasoning, even with extensive multimodal pretraining.
We introduce a simple method, whiteboard-of-thought prompting, to unlock the
visual reasoning capabilities of multimodal large language models across
modalities. Whiteboard-of-thought prompting provides multimodal large language
models with a metaphorical `whiteboard' to draw out reasoning steps as images,
then returns these images back to the model for further processing. We find
this can be accomplished with no demonstrations or specialized modules, instead
leveraging models' existing ability to write code with libraries such as
Matplotlib and Turtle. This simple approach shows state-of-the-art results on
four difficult natural language tasks that involve visual and spatial
reasoning. We identify multiple settings where GPT-4o using chain-of-thought
fails dramatically, including more than one where it achieves $0\%$ accuracy,
while whiteboard-of-thought enables up to $92\%$ accuracy in these same
settings. We present a detailed exploration of where the technique succeeds as
well as its sources of error.
