title: G-FOCUS: Towards a Robust Method for Assessing UI Design Persuasiveness
summary: 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.
paper_authors._id: 6821771ddf190eabf5f666d8
paper_authors.name: Jaehyun Jeon
paper_authors.status: claimed_verified
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paper_authors.fullname: Jaehyun Jeon
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paper_authors.user: jeochris
paper_authors._id: 6821771ddf190eabf5f666d9
paper_authors.name: Jang Han Yoon
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paper_authors.fullname: Jang Han Yoon
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paper_authors.user: jeffrobot
paper_authors._id: 6821771ddf190eabf5f666da
paper_authors.name: Min Soo Kim
paper_authors._id: 6821771ddf190eabf5f666db
paper_authors.name: Sumin Shim
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paper_authors._id: 6548fceac8f267c7c40c85d9
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paper_authors.fullname: SUMIN SHIM
paper_authors.type: user
paper_authors.user: use08174
paper_authors._id: 6821771ddf190eabf5f666dc
paper_authors.name: Yejin Choi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-12T13:16:57.521Z
paper_authors._id: 64d42729f63b01b7f676b176
paper_authors.avatarUrl: /avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
paper_authors.fullname: Yejin Choi
paper_authors.type: user
paper_authors.user: yejinchoinka
paper_authors._id: 6821771ddf190eabf5f666dd
paper_authors.name: Hanbin Kim
paper_authors._id: 6821771ddf190eabf5f666de
paper_authors.name: Youngjae Yu
paper_title: G-FOCUS: Towards a Robust Method for Assessing UI Design Persuasiveness
paper_summary: 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.
paper_ai_summary: G-FOCUS, a novel inference-time reasoning strategy, enhances Vision-Language Models for assessing UI persuasiveness, complementing A/B testing.
paper_ai_keywords: Vision-Language Models Pairwise UI Design Persuasiveness Assessment G-FOCUS position bias evaluation accuracy scalable UI preference modeling design optimization