  Chain-of-thought (CoT) reasoning has exhibited impressive performance in
language models for solving complex tasks and answering questions. However,
many real-world questions require multi-modal information, such as text and
images. Previous research on multi-modal CoT has primarily focused on
extracting fixed image features from off-the-shelf vision models and then
fusing them with text using attention mechanisms. This approach has limitations
because these vision models were not designed for complex reasoning tasks and
do not align well with language thoughts. To overcome this limitation, we
introduce a novel approach for multi-modal CoT reasoning that utilizes latent
space learning via diffusion processes to generate effective image features
that align with language thoughts. Our method fuses image features and text
representations at a deep level and improves the complex reasoning ability of
multi-modal CoT. We demonstrate the efficacy of our proposed method on
multi-modal ScienceQA and machine translation benchmarks, achieving
state-of-the-art performance on ScienceQA. Overall, our approach offers a more
robust and effective solution for multi-modal reasoning in language models,
enhancing their ability to tackle complex real-world problems.
