Multimodal Graph-of-Thoughts: How Text, Images, and Graphs Lead to Better Reasoning

Graph-of-Thought (GoT) prompting extends Chain-of-Thought and Tree-of-Thought approaches by representing model-generated ideas as an interconnected graph, allowing relationships across separate reasoning paths. The article examines a multimodal GoT architecture by Yao et al. that answers multiple-choice math and science questions using text, optional images, and a knowledge graph constructed from extracted subject-verb-object triplets that are consolidated through coreference resolution. A Graph Attention Network encodes the resulting graph, while attention and gated fusion mechanisms combine graph, text, and image representations before a decoder generates both a rationale and an answer. Tests on GSM8K and ScienceQA found that GoT-T5 models outperformed several smaller or comparable baselines, including UnifiedQA, GPT-3, GPT-3.5, and multimodal Chain-of-Thought systems in many categories, though GPT-4 performed better on GSM8K. Ablation experiments suggested that meaningful graph structure contributed to accuracy, but the relatively small performance decline from randomized graphs leaves its precise impact uncertain, and the approach’s additional training complexity and compute costs may limit its usefulness where modest accuracy gains do not justify the expense.

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