Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning
Persuasive visuals play a central role in advertising, public communication, and online media, making it increasingly important to understand whether an image is persuasive and why. However, current Multimodal Large Language Models (MLLMs) have limited ability to reason about visual persuasion. Our analysis reveals that models often rely on a shortcut---selectively citing easily recognizable visual elements and treating their mere presence as evidence of persuasiveness---rather than reasoning over the diverse visual cues in an image to determine how and why they contribute to persuasiveness. To address this limitation, we propose (1) a fine-tuning approach that trains models on rationales...
| Authors | Naeun Lee, Hyunjong Kim, Sunghwan Choi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 May 2026 |
| Last updated at source | 8 October 2026 |
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