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

  • 会议论文

  • 发表日期：

    2025年

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  • Chuan Meng,
  • Guglielmo Faggioli,
  • Mohammad Aliannejadi,
  • Nicola Ferro,
  • Josiane Mothe

摘要

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.