Multimodal Chain-of-Thought Reasoning in Language Models

Understand

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.