62 citations · 167 across the 17 of their papers we have counts for
19 papers
Exposure-Based Reinforcement Learning to Rank
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1
Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.g., from precision or discounted cumulative gain to fairness-of-exposure or…
Optimizing Compound Retrieval Systems
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1
Modern retrieval systems do not rely on a single ranking model to construct their rankings. Instead, they generally take a cascading approach where a sequence of ranking models are…
Inference Scaling for Long-Context Retrieval Augmented Generation
Zhenrui Yue, Honglei Zhuang, Aijun Bai +7
The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased c…
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +2
The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in gene…
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
Le Yan, Zhen Qin, Honglei Zhuang +4
The powerful generative abilities of large language models (LLMs) show potential in generating relevance labels for search applications. Previous work has found that directly askin…
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
Minghan Li, Honglei Zhuang, Kai Hui +5
Query expansion has been widely used to improve the search results of first-stage retrievers, yet its influence on second-stage, cross-encoder rankers remains under-explored. A rec…