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Multimodal reasoning is a critical component in the pursuit of artificialintelligence systems that exhibit human-like intelligence, especially whentackling complex tasks. While the chain-of-thought (CoT) technique has gainedconsiderable attention, the existing ScienceQA dataset, which focuses onmultimodal scientific questions and explanations from elementary and highschool textbooks, lacks a comprehensive evaluation of diverse approaches. Toaddress this gap, we present COCO Multi-Modal Reasoning(COCO-MMR) dataset, anovel dataset that encompasses an extensive collection of open-ended questions,rationales, and answers derived from the large object dataset COCO. Unlikeprevious datasets that rely on multiple-choice questions, our dataset pioneersthe use of open-ended questions in the context of multimodal CoT, introducing amore challenging problem that effectively assesses the reasoning capability ofCoT models. Through comprehensive evaluations and detailed analyses, we providevaluable insights and propose innovative techniques, including multi-hopcross-modal attention and sentence-level contrastive learning, to enhance theimage and text encoders. Extensive experiments demonstrate the efficacy of theproposed dataset and techniques, offering novel perspectives for advancingmultimodal reasoning. The data and code are available at\href{https://github.com/weijingxuan/COCO-MMR}{https://github.com/weijingxuan/COCO-MMR}.

Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework