Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks
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Computer Science > Computation and Language
Title:Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks
Abstract:Vision Language Models (VLMs) have demonstrated exceptional performance across various tasks. However, they have not yet been thoroughly evaluated on more complex tasks. The Persuasion Model, conceived by Aristotle, resembles a triangle shape, which highlights its inherent challenges related to personal biases. To assess the progress of VLMs on these complex tasks, we use the ImageArg datasets, focusing on the Logos, Ethos, and Pathos detection tasks. Our findings indicate that models from the Qwen family achieve improved F1 scores, with Qwen3 performing exceptionally well on the Logos and Pathos tasks, while Qwen2 exhibits competitive performance on the more complex Ethos detection task. We release the code to foster research in this direction.
| Subjects: | Computation and Language (cs.CL); Multimedia (cs.MM) |
| Cite as: | arXiv:2608.01238 [cs.CL] |
| (or arXiv:2608.01238v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.01238
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Khondoker Ittehadul Islam [view email][v1] Sun, 2 Aug 2026 13:47:32 UTC (1,329 KB)
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