MMPersuade: A Dataset and Evaluation Framework for Multimodal Persuasion
As Large Vision-Language Models (LVLMs) are increasingly deployed in domains
such as shopping, health, and news, they are exposed to pervasive persuasive
content. A critical question is how these models function as persuadees-how and
why they can be influenced by persuasive multimodal inputs. Understanding both
their susceptibility to persuasion and the effectiveness of different
persuasive strategies is crucial, as overly persuadable models may adopt
misleading beliefs, override user preferences, or generate unethical or unsafe
outputs when exposed to manipulative messages. We introduce MMPersuade, a
unified framework for systematically studying multimodal persuasion dynamics in
LVLMs. MMPersuade contributes (i) a comprehensive multimodal dataset that pairs
images and videos with established persuasion principles across commercial,
subjective and behavioral, and adversarial contexts, and (ii) an evaluation
framework that quantifies both persuasion effectiveness and model
susceptibility via third-party agreement scoring and self-estimated token
probabilities on conversation histories. Our study of six leading LVLMs as
persuadees yields three key insights: (i) multimodal inputs substantially
increase persuasion effectiveness-and model susceptibility-compared to text
alone, especially in misinformation scenarios; (ii) stated prior preferences
decrease susceptibility, yet multimodal information maintains its persuasive
advantage; and (iii) different strategies vary in effectiveness across
contexts, with reciprocity being most potent in commercial and subjective
contexts, and credibility and logic prevailing in adversarial contexts. By
jointly analyzing persuasion effectiveness and susceptibility, MMPersuade
provides a principled foundation for developing models that are robust,
preference-consistent, and ethically aligned when engaging with persuasive
multimodal content.
6901199c646208eac0d1f151
Haoyi Qiu
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2025-10-29T12:47:34.998Z
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Haoyi Qiu
haoyiq114
user
haoyiq114
6901199c646208eac0d1f152
Yilun Zhou
6901199c646208eac0d1f153
Pranav Narayanan Venkit
6901199c646208eac0d1f154
Kung-Hsiang Huang
claimed_verified
2025-10-29T12:47:37.482Z
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/avatars/5f276e27e3907d3898cdbfcaa7c43516.svg
Kung-Hsiang Huang
khhuang
user
khhuang
6901199c646208eac0d1f155
Jiaxin Zhang
claimed_verified
2025-11-06T11:48:55.651Z
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/avatars/8c4906cf559941b1043a2a04a6456cd3.svg
Jiaxin Zhang
zhangjiaxin2012
user
zhangjiaxin2012
6901199c646208eac0d1f156
Nanyun Peng
6901199c646208eac0d1f157
Chien-Sheng Wu
MMPersuade: A Dataset and Evaluation Framework for Multimodal Persuasion
As Large Vision-Language Models (LVLMs) are increasingly deployed in domains
such as shopping, health, and news, they are exposed to pervasive persuasive
content. A critical question is how these models function as persuadees-how and
why they can be influenced by persuasive multimodal inputs. Understanding both
their susceptibility to persuasion and the effectiveness of different
persuasive strategies is crucial, as overly persuadable models may adopt
misleading beliefs, override user preferences, or generate unethical or unsafe
outputs when exposed to manipulative messages. We introduce MMPersuade, a
unified framework for systematically studying multimodal persuasion dynamics in
LVLMs. MMPersuade contributes (i) a comprehensive multimodal dataset that pairs
images and videos with established persuasion principles across commercial,
subjective and behavioral, and adversarial contexts, and (ii) an evaluation
framework that quantifies both persuasion effectiveness and model
susceptibility via third-party agreement scoring and self-estimated token
probabilities on conversation histories. Our study of six leading LVLMs as
persuadees yields three key insights: (i) multimodal inputs substantially
increase persuasion effectiveness-and model susceptibility-compared to text
alone, especially in misinformation scenarios; (ii) stated prior preferences
decrease susceptibility, yet multimodal information maintains its persuasive
advantage; and (iii) different strategies vary in effectiveness across
contexts, with reciprocity being most potent in commercial and subjective
contexts, and credibility and logic prevailing in adversarial contexts. By
jointly analyzing persuasion effectiveness and susceptibility, MMPersuade
provides a principled foundation for developing models that are robust,
preference-consistent, and ethically aligned when engaging with persuasive
multimodal content.
MMPersuade is a framework for studying multimodal persuasion in Large Vision-Language Models, revealing insights into their susceptibility and the effectiveness of various persuasive strategies across different contexts.
Large Vision-Language Models LVLMs multimodal persuasion MMPersuade multimodal dataset persuasion principles third-party agreement scoring self-estimated token probabilities conversation histories misinformation scenarios reciprocity credibility logic