可视化会误导多模态大模型判断网络中是否存在连接桥。
Visualization Biases MLLM's Decision Making in Network Data Tasks
- 用可视化替代结构化文本，反而让模型更自信
- 标准可视化导致模型误判桥梁存在或不存在
- 提醒开发者警惕生成式AI中的视觉幻觉风险
我们评估了可视化对多模态大模型判断网络中是否存在桥接结构的影响。结果显示，与理论上有助于答题的结构化文本相比，加入可视化反而提升了模型的自信度。然而，我们发现标准可视化技术会强烈引导模型接受或否定桥的存在，且这种倾向与实际网络结构无关。尽管可视化能有效影响模型判断而保持其高自信度，但也暗示使用者需谨慎在生成式AI应用中引入可视化，以避免产生不实结论。
原文摘要 · Abstract (English)
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.
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