  A long-standing goal of AI systems is to perform complex multimodal reasoning
like humans. Recently, large language models (LLMs) have made remarkable
strides in such multi-step reasoning on the language modality solely by
leveraging the chain of thought (CoT) to mimic human thinking. However, the
transfer of these advancements to multimodal contexts introduces heightened
challenges, including but not limited to the impractical need for
labor-intensive annotation and the limitations in terms of flexibility,
generalizability, and explainability. To evoke CoT reasoning in multimodality,
this work first conducts an in-depth analysis of these challenges posed by
multimodality and presents two key insights: "keeping critical thinking" and
"letting everyone do their jobs" in multimodal CoT reasoning. Furthermore, this
study proposes a novel DDCoT prompting that maintains a critical attitude
through negative-space prompting and incorporates multimodality into reasoning
by first dividing the reasoning responsibility of LLMs into reasoning and
recognition and then integrating the visual recognition capability of visual
models into the joint reasoning process. The rationales generated by DDCoT not
only improve the reasoning abilities of both large and small language models in
zero-shot prompting and fine-tuning learning, significantly outperforming
state-of-the-art methods but also exhibit impressive generalizability and
explainability.
