让设计师用自然语言协作优化设计参数，提升控制感与效率。
Cooperative Design Optimization through Natural Language Interaction
- 结合大模型与贝叶斯优化，支持自然语言交互干预
- 用户自主性显著高于纯系统主导方法，性能接近人工设计
- 适合希望参与优化过程的设计师，降低认知负担
设计成功交互需确定最优参数，传统方法依赖反复用户测试与试错，需在高维空间平衡多重目标，耗时且费脑力。现有系统主导的优化方法（如贝叶斯优化）虽可推荐下一步测试参数，但缺乏设计师介入机会，影响体验。本文提出一种通过自然语言交互实现人机协同的设计优化框架，将系统主导优化与大语言模型（LLM）结合，使设计师能主动干预并理解系统推理。实验表明，该方法显著提升用户控制感，优化性能优于系统主导方法，接近人工设计水平；同时在性能相当情况下，认知负荷低于现有协同方法。
原文摘要 · Abstract (English)
Designing successful interactions requires identifying optimal design parameters. To do so, designers often conduct iterative user testing and exploratory trial-and-error. This involves balancing multiple objectives in a high-dimensional space, making the process time-consuming and cognitively demanding. System-led optimization methods, such as those based on Bayesian optimization, can determine for designers which parameters to test next. However, they offer limited opportunities for designers to intervene in the optimization process, negatively impacting the designer's experience. We propose a design optimization framework that enables natural language interactions between designers and the optimization system, facilitating cooperative design optimization. This is achieved by integrating system-led optimization methods with Large Language Models (LLMs), allowing designers to intervene in the optimization process and better understand the system's reasoning. Experimental results show that our method provides higher user agency than a system-led method and shows promising optimization performance compared to manual design. It also matches the performance of an existing cooperative method with lower cognitive load.
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