Query performance prediction (QPP) aims to estimate the retrieval quality of a search system
for a query without human relevance judgments. Previous QPP methods typically return a …

Query Performance Prediction is a key task in IR, focusing on estimating the retrieval quality
of a given query without relying on human-labeled relevance judgments. Over the decades …

Query performance prediction (QPP) is a core task in information retrieval (IR) that aims at
predicting the retrieval quality for a given query without relevance judgments. QPP has been …

Research has already found that many retrieval methods are sensitive to the choice and
order of terms that appear in a query, which can significantly impact retrieval effectiveness …

Prior research has demonstrated that reformulation of queries can significantly enhance
retrieval effectiveness. Despite notable successes in neural-based query reformulation …

The state-of-the-art query performance prediction methods rely on the fine-tuning of
contextual language models to estimate retrieval effectiveness on a per-query basis. Our …

Abstract Query Performance Prediction (QPP) aims to estimate the effectiveness of a query
in addressing the underlying information need without any relevance judgments. More …

This paper introduces ADG-QPP (Adaptive Disturbance Generation), an unsupervised
Query Performance Prediction (QPP) method designed specifically for dense neural …

Large language Models (LLMs) are highly sensitive to variations in prompt formulation,
which can significantly impact their ability to generate accurate responses. In this paper, we …

Research has already found that many retrieval methods are sensitive to the choice and
order of terms that appear in a query, which can significantly impact retrieval effectiveness …