Multimodal Chain-of-Thought Reasoning in Language Models

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

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: Chain of Thought Prompting, Language Models, Multimodal Reasoning, Fine-tuning, Natural Language Processing.

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TL;DR: To elicit CoT reasoning in multimodality, we propose Multimodal-CoT that incorporates vision features in a decoupled training framework. The approach offers the advantages of mitigating hallucination and enhancing convergence speed.

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 new state-of-the-art performance on the ScienceQA benchmark. Our analysis indicates that Multimodal-CoT offers the advantages of mitigating hallucination. Code is publicly available at Anonymous.

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Submission Number: 5567