463 citations · 904 across the 24 of their papers we have counts for
6 papers · 1 filter
Learning to Rank when Grades Matter
Le Yan, Zhen Qin, Gil Shamir +3
Graded labels are ubiquitous in real-world learning-to-rank applications, especially in human rated relevance data. Traditional learning-to-rank techniques aim to optimize the rank…
RD-Suite: A Benchmark for Ranking Distillation
Zhen Qin, Rolf Jagerman, Rama Pasumarthi +6
The distillation of ranking models has become an important topic in both academia and industry. In recent years, several advanced methods have been proposed to tackle this problem,…
LibAUC: A Deep Learning Library for X-Risk Optimization
Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu +3
This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-…
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Zhen Qin, Rolf Jagerman, Kai Hui +9
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem. However, researc…
Query Expansion by Prompting Large Language Models
Rolf Jagerman, Honglei Zhuang, Zhen Qin +2
Query expansion is a widely used technique to improve the recall of search systems. In this paper, we propose an approach to query expansion that leverages the generative abilities…
Metric-agnostic Ranking Optimization
Qingyao Ai, Xuanhui Wang, Michael Bendersky
Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of rank…