Large language models (LLMs)  have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT)  prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies have focused on the language modality. We propose Multimodal-CoT that incorporates language (text)  and vision (images)  modalities into a two-stage framework that separates rationale generation and answer inference.
In this way, answer inference can leverage better generated rationales that are based on multimodal information.
With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5)  by 16 percentage points (75.17%91.68% accuracy)  and even surpasses human performance on the ScienceQA benchmark.
Code is publicly available.111https://github.com/amazon-science/mm-cot