13 citations · 29 across the 13 of their papers we have counts for
15 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…
The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
Aileen Cheng, Alon Jacovi, Amir Globerson +62
We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually…
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…
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks
Paul Suganthan, Fedor Moiseev, Le Yan +7
Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language proc…
Searching Personal Collections
Michael Bendersky, Donald Metzler, Marc Najork +1
This article describes the history of information retrieval on personal document collections.
Integrating Planning into Single-Turn Long-Form Text Generation
Yi Liang, You Wu, Honglei Zhuang +8
Generating high-quality, in-depth textual documents, such as academic papers, news articles, Wikipedia entries, and books, remains a significant challenge for Large Language Models…
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…
LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
Zhen Qin, Junru Wu, Jiaming Shen +2
We introduce LAMPO, a novel paradigm that leverages Large Language Models (LLMs) for solving few-shot multi-class ordinal classification tasks. Unlike conventional methods, which 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…
LiPO: Listwise Preference Optimization through Learning-to-Rank
Tianqi Liu, Zhen Qin, Junru Wu +9
Aligning language models (LMs) with curated human feedback is critical to control their behaviors in real-world applications. Several recent policy optimization methods, such as DP…
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…