  The potential of Vision-Language Models (VLMs) often remains underutilized in
handling complex text-based problems, particularly when these problems could
benefit from visual representation. Resonating with humans' ability to solve
complex text-based problems by (1) creating a visual diagram from the problem
and (2) deducing what steps they need to take to solve it, we propose
Self-Imagine. We leverage a single Vision-Language Model (VLM) to generate a
structured representation of the question using HTML, then render the HTML as
an image, and finally use the same VLM to answer the question using both the
question and the image. Our approach does not require any additional training
data or training. We evaluate our approach on three mathematics tasks and nine
general-purpose reasoning tasks using state-of-the-art (LLAVA-1.5 and GEMINI
PRO) VLMs. Our approach boosts the performance of LLAVA-1.5 and GEMINI PRO on
all math tasks (on average GSM8K: +3.1%; ASDIV: +3.2%; SVAMP: +6.9%) and the
majority of the general-purpose reasoning tasks by 3.2% to 6.0% on average.
