The ability to predict the performance of a query before results are returned has been a longstanding challenge in Information Retrieval (IR) systems. Inspired by this task, we introduce, in this paper, a novel task called "Prompt Performance Prediction" (PPP) that aims to predict the performance of a prompt, before obtaining the actual generated images. We demonstrate the plausibility of our task by measuring the correlation coefficient between predicted and actual performance scores across: three datasets containing pairs of prompts and generated images AND three art domain datasets of real images and real user appreciation ratings. Our results show promising performance prediction capabilities, suggesting potential applications for optimizing user prompts.

翻译：在信息检索（IR）系统中，在得到查询结果之前预测其性能的能力一直是一项长期挑战。受此任务启发，我们在本文中提出了一项名为“提示性能预测”（PPP）的新任务，旨在获取实际生成图像之前预测提示的性能。我们通过测量三个包含提示与生成图像配对的数据集以及三个包含真实图像与真实用户评价评分的美术领域数据集上，预测性能分数与实际性能分数之间的相关系数，证明了该任务的可行性。我们的结果展示了有前景的性能预测能力，暗示了优化用户提示的潜在应用。