Query Performance Prediction

Query performance prediction (QPP) aims to estimate the effectiveness of a search query before execution, eliminating the need for computationally expensive full retrievals. Current research focuses on developing accurate QPP models for diverse search modalities, including text, images, and multi-hop question answering, often employing machine learning techniques like stacked LSTMs and leveraging large language models for automated relevance judgment. These advancements are crucial for optimizing search engine performance, enabling efficient resource allocation, and improving the user experience across various applications, from database systems to large-scale information retrieval.