mlsanityData to 14 Oct 2026

Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks

arXiv 2608.01238 ↗First posted 2 Aug 2026cs.CL cs.MMcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 10 of its 15 references that are in the map. First posted on arXiv 2 Aug 2026.

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10 of 15 references.

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  1. 2024An Introduction to Vision-Language Modeling27 May

Other references · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2025Qwen3 Technical Reportcited by 8,820
  5. 2015VQA: Visual Question Answeringcited by 2,506
  6. 2023Intriguing properties of generative classifierscited by 52
  7. 2022ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Miningcited by 12
  8. 2020Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Modelscited by 3
  9. 2025Joint Effects of Argumentation Theory, Audio Modality and Data Enrichment on LLM-Based Fallacy Classificationcited by 1

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