We evaluate how visualizations can influence the judgment of MLLMs about the presence or absence of bridges in a network. We show that the inclusion of visualization improves confidence over a structured text-based input that could theoretically be helpful for answering the question. On the other hand, we observe that standard visualization techniques create a strong bias towards accepting or refuting the presence of a bridge -- independently of whether or not a bridge actually exists in the network. While our results indicate that the inclusion of visualization techniques can effectively influence the MLLM's judgment without compromising its self-reported confidence, they also imply that practitioners must be careful of allowing users to include visualizations in generative AI applications so as to avoid undesired hallucinations.

翻译：我们评估了可视化如何影响多模态大语言模型对网络中桥接结构存在与否的判断。研究表明，相较于理论上可能有助于解答问题的结构化文本输入，引入可视化能提升模型的置信度。然而，我们观察到标准可视化技术会引发强烈偏向性——无论网络中是否实际存在桥接结构，模型都倾向于接受或否定其存在。虽然结果表明可视化技术的引入能有效影响多模态大语言模型的判断且不损害其自报告置信度，但这也意味着实践者需谨慎允许用户在生成式人工智能应用中引入可视化，以避免产生非预期的幻觉现象。