G-FOCUS: Towards a Robust Method for Assessing UI Design Persuasiveness
Evaluating user interface (UI) design effectiveness extends beyond aesthetics
to influencing user behavior, a principle central to Design Persuasiveness. A/B
testing is the predominant method for determining which UI variations drive
higher user engagement, but it is costly and time-consuming. While recent
Vision-Language Models (VLMs) can process automated UI analysis, current
approaches focus on isolated design attributes rather than comparative
persuasiveness-the key factor in optimizing user interactions. To address this,
we introduce WiserUI-Bench, a benchmark designed for Pairwise UI Design
Persuasiveness Assessment task, featuring 300 real-world UI image pairs labeled
with A/B test results and expert rationales. Additionally, we propose G-FOCUS,
a novel inference-time reasoning strategy that enhances VLM-based
persuasiveness assessment by reducing position bias and improving evaluation
accuracy. Experimental results show that G-FOCUS surpasses existing inference
strategies in consistency and accuracy for pairwise UI evaluation. Through
promoting VLM-driven evaluation of UI persuasiveness, our work offers an
approach to complement A/B testing, propelling progress in scalable UI
preference modeling and design optimization. Code and data will be released
publicly.
6821771ddf190eabf5f666d8
Jaehyun Jeon
claimed_verified
2025-05-12T06:50:17.832Z
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Jaehyun Jeon
user
jeochris
6821771ddf190eabf5f666d9
Jang Han Yoon
admin_assigned
2025-05-12T13:16:34.355Z
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Jang Han Yoon
user
jeffrobot
6821771ddf190eabf5f666da
Min Soo Kim
6821771ddf190eabf5f666db
Sumin Shim
admin_assigned
2025-05-12T13:16:50.404Z
6548fceac8f267c7c40c85d9
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SUMIN SHIM
user
use08174
6821771ddf190eabf5f666dc
Yejin Choi
admin_assigned
2025-05-12T13:16:57.521Z
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/avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
Yejin Choi
user
yejinchoinka
6821771ddf190eabf5f666dd
Hanbin Kim
6821771ddf190eabf5f666de
Youngjae Yu
G-FOCUS: Towards a Robust Method for Assessing UI Design Persuasiveness
Evaluating user interface (UI) design effectiveness extends beyond aesthetics
to influencing user behavior, a principle central to Design Persuasiveness. A/B
testing is the predominant method for determining which UI variations drive
higher user engagement, but it is costly and time-consuming. While recent
Vision-Language Models (VLMs) can process automated UI analysis, current
approaches focus on isolated design attributes rather than comparative
persuasiveness-the key factor in optimizing user interactions. To address this,
we introduce WiserUI-Bench, a benchmark designed for Pairwise UI Design
Persuasiveness Assessment task, featuring 300 real-world UI image pairs labeled
with A/B test results and expert rationales. Additionally, we propose G-FOCUS,
a novel inference-time reasoning strategy that enhances VLM-based
persuasiveness assessment by reducing position bias and improving evaluation
accuracy. Experimental results show that G-FOCUS surpasses existing inference
strategies in consistency and accuracy for pairwise UI evaluation. Through
promoting VLM-driven evaluation of UI persuasiveness, our work offers an
approach to complement A/B testing, propelling progress in scalable UI
preference modeling and design optimization. Code and data will be released
publicly.
G-FOCUS, a novel inference-time reasoning strategy, enhances Vision-Language Models for assessing UI persuasiveness, complementing A/B testing.
Vision-Language Models Pairwise UI Design Persuasiveness Assessment G-FOCUS position bias evaluation accuracy scalable UI preference modeling design optimization