13 citations · 29 across the 13 of their papers we have counts for
8 papers · 1 filter
Harnessing Pairwise Ranking Prompting Through Sample-Efficient Ranking Distillation
Junru Wu, Le Yan, Zhen Qin +6
While Pairwise Ranking Prompting (PRP) with Large Language Models (LLMs) is one of the most effective zero-shot document ranking methods, it has a quadratic computational complexit…
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…
Searching Personal Collections
Michael Bendersky, Donald Metzler, Marc Najork +1
This article describes the history of information retrieval on personal document collections.
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…
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai +8
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method,…
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
Honglei Zhuang, Zhen Qin, Kai Hui +4
Zero-shot text rankers powered by recent LLMs achieve remarkable ranking performance by simply prompting. Existing prompts for pointwise LLM rankers mostly ask the model to choose…