Introduction to the Special Issue on Query Performance Prediction
Abstract
Query Performance Prediction (QPP) aims to estimate the effectiveness of a retrieval system for a given query without requiring relevance judgments. While traditionally studied for sparse retrieval, recent advances in neural ranking, dense retrieval, and large language models (LLMs) have prompted a shift towards QPP methods that better reflect modern IR systems. This Special Issue on Query Performance Prediction Towards Novel Information Retrieval Paradigms presents recent advances along two complementary directions. The first focuses on LLM- and representation-based approaches that leverage query variants, semantic interactions, and comparative assessment to improve prediction for neural retrieval models. The second explores new formulations and application settings, including inverse learning, prediction of reliability in retrieval-augmented generation, and the incorporation of multimodal and neurophysiological signals. These works highlight emerging challenges in evaluating QPP in neural and generative settings by demonstrating that QPP is evolving into a broader framework for estimating uncertainty and reliability across complex IR pipelines. We believe that this Special Issue will foster further research towards robust and generalizable QPP methods for next-generation information access systems.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.