The ability to predict the performance of a query in Information Retrieval (IR) systems has been a longstanding challenge. In this paper, we introduce a novel task called "Prompt Performance Prediction" that aims to predict the performance of a query, referred to as a prompt, before obtaining the actual search results. The context of our task leverages a generative model as an IR engine to evaluate the prompts' performance on image retrieval tasks. 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. Our results show promising performance prediction capabilities, suggesting potential applications for optimizing generative IR systems.

翻译：在信息检索系统中，预测查询性能的能力一直是一项长期挑战。本文提出一项名为"提示性能预测"的新任务，旨在获取实际搜索结果之前预测查询（称为提示）的性能。该任务背景利用生成式模型作为信息检索引擎，评估提示在图像检索任务中的表现。我们通过测量三个包含提示与生成图像配对数据集中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。实验结果表明，该方法具有良好的性能预测能力，为优化生成式信息检索系统提供了潜在应用前景。