  Multimodal reasoning stands as a pivotal capability for large vision-language
models (LVLMs). The integration with Domain-Specific Languages (DSL), offering
precise visual representations, equips these models with the opportunity to
execute more accurate reasoning in complex and professional domains. However,
the vanilla Chain-of-Thought (CoT) prompting method faces challenges in
effectively leveraging the unique strengths of visual and DSL representations,
primarily due to their differing reasoning mechanisms. Additionally, it often
falls short in addressing critical steps in multi-step reasoning tasks. To
mitigate these challenges, we introduce the \underline{B}i-Modal
\underline{B}ehavioral \underline{A}lignment (BBA) prompting method, designed
to maximize the potential of DSL in augmenting complex multi-modal reasoning
tasks. This method initiates by guiding LVLMs to create separate reasoning
chains for visual and DSL representations. Subsequently, it aligns these chains
by addressing any inconsistencies, thus achieving a cohesive integration of
behaviors from different modalities. Our experiments demonstrate that BBA
substantially improves the performance of GPT-4V(ision) on geometry problem
solving ($28.34\% \to 34.22\%$), chess positional advantage prediction
($42.08\% \to 46.99\%$) and molecular property prediction ($77.47\% \to
83.52\%$).
