CrowdGenUI：将基于LLM的UI生成与众包用户偏好对齐
CrowdGenUI: Aligning LLM-Based UI Generation with Crowdsourced User Preferences
AI总结：
针对LLM生成UI缺乏用户偏好理解的问题，提出CrowdGenUI框架，利用众包偏好引导LLM推理，经用户研究验证能更好匹配用户意图。
摘要：
大型语言模型（LLMs）已在包括用户界面（UI）生成在内的各种设计领域展现出非凡潜力。然而，当前用于UI生成的LLM往往提供通用解决方案，缺乏对任务上下文和用户偏好的细致理解。我们提出CrowdGenUI，一个利用众包用户偏好增强基于LLM的UI生成的框架。该框架通过引导LLM推理融入真实用户偏好来解决上述局限，从而生成反映用户需求和特定任务要求的UI组件。我们在图像编辑领域评估了该框架，从50名参与者处收集了包含720条用户偏好的偏好库，涵盖各种UI组件的可预测性、效率和可探索性等偏好。一项用户研究（N=78）表明，与仅由LLM生成的UI相比，使用我们偏好引导框架生成的UI能更好地匹配用户意图，凸显了我们所提框架的有效性。我们进一步讨论了研究结果，并为未来关于基于LLM的以用户为中心的UI生成研究提出了见解。
英文摘要：
Large Language Models (LLMs) have demonstrated remarkable potential across various design domains, including user interface (UI) generation. However, current LLMs for UI generation tend to offer generic solutions that lack a nuanced understanding of task context and user preferences. We present CrowdGenUI, a framework that enhances LLM-based UI generation with crowdsourced user preferences. This framework addresses the limitations by guiding LLM reasoning with real user preferences, enabling the generation of UI widgets that reflect user needs and task-specific requirements. We evaluate our framework in the image editing domain by collecting a library of 720 user preferences from 50 participants, covering preferences such as predictability, efficiency, and explorability of various UI widgets. A user study (N=78) demonstrates that UIs generated with our preference-guided framework can better match user intentions compared to those generated by LLMs alone, highlighting the effectiveness of our proposed framework. We further discuss the study findings and present insights for future research on LLM-based user-centered UI generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
