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 primarily 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. Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach. With Multimodal-CoT, our model under 1 billion parameters achieves state-of-the-art performance on the ScienceQA benchmark. Our analysis indicates that Multimodal-CoT offers the advantages of mitigating hallucination and enhancing convergence speed. Code is publicly available at https://github.com/amazon-science/mm-cot.

翻译：大型语言模型（LLMs）通过链式推理（CoT）提示生成中间推理链作为推断答案的依据，在复杂推理任务中展现出卓越性能。然而，现有CoT研究主要聚焦于语言模态。我们提出多模态链式推理（Multimodal-CoT），将语言（文本）与视觉（图像）模态融合到一个两阶段框架中，将理性生成与答案推理分离。通过这种方式，答案推理能够利用基于多模态信息生成的更优理性。在ScienceQA和A-OKVQA基准数据集上的实验结果表明了所提方法的有效性。采用Multimodal-CoT，我们参数量低于10亿的模型在ScienceQA基准上达到了最先进性能。分析表明，Multimodal-CoT具有减少幻觉和加速收敛的优势。代码已公开于https://github.com/amazon-science/mm-cot。