Generative models are increasingly powerful, yet users struggle to guide them through prompts. The generative process is difficult to control and unpredictable, and user instructions may be ambiguous or under-specified. Prior prompt refinement tools heavily rely on human effort, while prompt optimization methods focus on numerical functions and are not designed for human-centered generative tasks, where feedback is better expressed as binary preferences and demands convergence within few iterations. We present APPO, a preference-guided prompt optimization algorithm. Instead of iterating prompts, users only provide binary preferential feedback. APPO adaptively balances its strategies between exploiting user feedback and exploring new directions, yielding effective and efficient optimization. We evaluate APPO on image generation, and the results show APPO enables achieving satisfactory outcomes in fewer iterations with lower cognitive load than manual prompt editing. We anticipate APPO will advance human-AI collaboration in generative tasks by leveraging user preferences to guide complex content creation.

翻译：生成模型日益强大，但用户仍难以通过提示有效引导它们。生成过程难以控制且不可预测，用户指令可能模糊或未充分指定。现有的提示优化工具严重依赖人工努力，而提示优化方法侧重于数值函数，并非为以人为中心的生成任务设计——在这类任务中，反馈更适合以二元偏好形式表达，并要求在少数迭代内收敛。我们提出了APPO，一种基于偏好引导的提示优化算法。用户无需迭代修改提示，仅需提供二元偏好反馈。APPO自适应地平衡利用用户反馈与探索新方向两种策略，从而实现高效且有效的优化。我们在图像生成任务上评估了APPO，结果表明，与手动提示编辑相比，APPO能以更少的迭代次数和更低的认知负荷实现令人满意的生成结果。我们预期APPO将通过利用用户偏好来引导复杂内容创作，推动生成任务中的人机协作向前发展。