  Multimodal reasoning is a challenging task that requires models to reason
across multiple modalities to answer questions. Existing approaches have made
progress by incorporating language and visual modalities into a two-stage
reasoning framework, separating rationale generation from answer inference.
However, these approaches often fall short due to the inadequate quality of the
generated rationales. In this work, we delve into the importance of rationales
in model reasoning. We observe that when rationales are completely accurate,
the model's accuracy significantly improves, highlighting the need for
high-quality rationale generation. Motivated by this, we propose MC-CoT, a
self-consistency training strategy that generates multiple rationales and
answers, subsequently selecting the most accurate through a voting process.
This approach not only enhances the quality of generated rationales but also
leads to more accurate and robust answers. Through extensive experiments, we
demonstrate that our approach significantly improves model performance across
various benchmarks. Remarkably, we show that even smaller base models, when
equipped with our proposed approach, can achieve results comparable to those of
larger models, illustrating the potential of our approach in harnessing the
power of rationales for improved multimodal reasoning. The code is available at
https://github.com/chengtan9907/mc-cot.

Artificial Intelligence