Prompt Performance Prediction for Generative IR

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.