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Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings
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Recent advances in large language models elicit reasoning in a chain-of-thought that allows models to decompose problems in a human-like fashion. Though this paradigm improves multi-step reasoning ability in language models, it is limited by being unimodal and applied mainly to question-answering tasks. We claim that incorporating visual augmentation into reasoning is essential, especially for complex, imaginative tasks. Consequently, we introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to recursively bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks that can benefit from temporal reasoning, as well as provide interpretability into models' multi-step reasoning. We apply VCoT to the Visual Storytelling and WikiHow summarization datasets and demonstrate through human evaluation that VCoT offers novel and consistent synthetic data augmentation beating chain-of-thought baselines, which can be used to enhance downstream performance.
Forward citations
Cited by 6 Pith papers
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Point-RFT: Improving Multimodal Reasoning with Visually Grounded Reinforcement Finetuning
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MagiC evaluates answer correctness, reasoning validity, grounding fidelity, and self-correction on about 900 hand-annotated visual questions across 15 vision-language models.
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Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?
A new 1,430-item multimodal benchmark shows that leading multimodal LLMs rarely notice small visual traps needed for commonsense safety reasoning, with top scores near 0.46 on a scale whose maximum is about 0.97.
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Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought
Argus adds explicit language-guided visual attention to multimodal LLMs by grounding questions to bounding boxes and re-engaging those regions, improving vision-centric reasoning and grounding accuracy.
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Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation
Difficulty-aware compression of CoT traces plus SFT and DPO lets LLMs shorten reasoning on easy math problems, cutting tokens by up to 30% with mixed accuracy effects.
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