no code implementations • 17 Apr 2024 • Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis
Our method takes both LLM generated relevance labels and pairwise preferences.
no code implementations • 15 Nov 2023 • Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky
In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers?
no code implementations • 30 Jun 2023 • Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem.
no code implementations • 5 May 2023 • Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, Michael Bendersky
Query expansion is a widely used technique to improve the recall of search systems.
no code implementations • 2 Nov 2022 • Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, Bing-Rong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design.
no code implementations • 12 Oct 2022 • Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT.
1 code implementation • 21 May 2020 • Rolf Jagerman, Maarten de Rijke
Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system.
1 code implementation • 2 Feb 2020 • Rolf Jagerman, Ilya Markov, Maarten de Rijke
Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.
no code implementations • 16 Jul 2019 • Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
Through randomization the effect of different types of bias can be removed from the learning process.
2 code implementations • 15 Jul 2019 • Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke
At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.
1 code implementation • 24 Jul 2017 • Rolf Jagerman, Julia Kiseleva, Maarten de Rijke
List-wise learning to rank methods are considered to be the state-of-the-art.
1 code implementation • 24 May 2016 • Rolf Jagerman, Carsten Eickhoff, Maarten de Rijke
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery.
no code implementations • 11 Nov 2015 • Jan Deriu, Rolf Jagerman, Kai-En Tsay
The problem of inpainting involves reconstructing the missing areas of an image.