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20242026
most citedTraining on the Test Model: Contamination in Ranking Distillation

1 citations · 4 across the 14 of their papers we have counts for

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cs.IR2026

A Picture of Agentic Search

Francesca Pezzuti, Ophir Frieder, Fabrizio Silvestri +2

With automated systems increasingly issuing search queries alongside humans, Information Retrieval (IR) faces a major shift. Yet IR remains human-centred, with systems, evaluation…

cs.IR2026

NeuCLIRTech: Chinese Monolingual and Cross-Language Information Retrieval Evaluation in a Challenging Domain

Dawn Lawrie, James Mayfield, Eugene Yang +6

Measuring advances in retrieval requires test collections with relevance judgments that can faithfully distinguish systems. This paper presents NeuCLIRTech, an evaluation collectio…

cs.IR2025

NeuCLIRBench: A Modern Evaluation Collection for Monolingual, Cross-Language, and Multilingual Information Retrieval

Dawn Lawrie, James Mayfield, Eugene Yang +6

To measure advances in retrieval, test collections with relevance judgments that can faithfully distinguish systems are required. This paper presents NeuCLIRBench, an evaluation co…

cs.IR2025

Overview of the TREC 2024 NeuCLIR Track

Dawn Lawrie, Sean MacAvaney, James Mayfield +4

The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the effect of neural approaches on cross-language information access. The tra…

cs.IR2025

Neural Prioritisation for Web Crawling

Francesca Pezzuti, Sean MacAvaney, Nicola Tonellotto

Given the vast scale of the Web, crawling prioritisation techniques based on link graph traversal, popularity, link analysis, and textual content are frequently applied to surface…

cs.IR2025

Disentangling Locality and Entropy in Ranking Distillation

Andrew Parry, Debasis Ganguly, Sean MacAvaney

The training process of ranking models involves two key data selection decisions: a sampling strategy, and a labeling strategy. Modern ranking systems, especially those for perform…