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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03617 |
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| _version_ | 1866918187372642304 |
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| author | Brand, Timo Förster, Henry Kobourov, Stephen G. Miller, Jacob |
| author_facet | Brand, Timo Förster, Henry Kobourov, Stephen G. Miller, Jacob |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03617 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visualization Biases MLLM's Decision Making in Network Data Tasks Brand, Timo Förster, Henry Kobourov, Stephen G. Miller, Jacob Graphics Artificial Intelligence 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. |
| title | Visualization Biases MLLM's Decision Making in Network Data Tasks |
| topic | Graphics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.03617 |