Multimodal Chain-of-Thought Reasoning in Language Models

Zhuosheng Zhang, Aston Zhang, Mu Li, hai zhao, George Karypis, Alex Smola

Abstract: 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.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/amazon-science/mm-cot

Assigned Action Editor: ~Alessandro_Sordoni1

Submission Number: 2124