Communication Dans Un Congrès Année : 2025

QPP++ 2025: Query Performance Prediction and its Applications in the Era of Large Language Models

Chuan Meng
Guglielmo Faggioli
Mohammad Aliannejadi
Nicola Ferro

Résumé

Query performance prediction (QPP) is a key task in information retrieval (IR) and has been studied for over a decade. The task of QPP is defined as estimating search effectiveness without human relevance judgments. In this workshop, we aim to bring together researchers and practitioners from academia and industry to discuss new perspectives on QPP. Amongst the limitations in the existing QPP literature, we can mention little work has focused on (i) predicting the performance of newly emerged large language model (LLM)-based retrievers/re-rankers or of generative AI systems, (ii) leveraging LLM to model QPP, (iii) investigating concrete applications of QPP, (iv) exploring QPP in the context of multi-modal content, and (v) Exploring multilingual QPP. Those are examples of topics that we encourage authors to contribute to in this workshop.

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hal-05375581 , version 1 (20-11-2025)

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Chuan Meng, Guglielmo Faggioli, Mohammad Aliannejadi, Nicola Ferro, Josiane Mothe. QPP++ 2025: Query Performance Prediction and its Applications in the Era of Large Language Models. European Conference on Information Retrieval (ECIR), 2025, LUCCA (Italy), France. pp.319-325, ⟨10.1007/978-3-031-88720-8_49⟩. ⟨hal-05375581⟩
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