Search Results for author: Xuanhui Wang

Found 24 papers, 5 papers with code

Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers

no code implementations15 Nov 2023 Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky

We first show that directly applying the expansion techniques in the current literature to state-of-the-art neural rankers can result in deteriorated zero-shot performance.

Instruction Following Language Modelling +1

PaRaDe: Passage Ranking using Demonstrations with Large Language Models

no code implementations22 Oct 2023 Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui

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, such as BM25, are rated and reordered to improve relevance.

Passage Ranking Passage Re-Ranking +6

Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

no code implementations21 Oct 2023 Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang, Michael Bendersky

We propose to incorporate fine-grained relevance labels into the prompt for LLM rankers, enabling them to better differentiate among documents with different levels of relevance to the query and thus derive a more accurate ranking.

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

no code implementations30 Jun 2023 Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky

On TREC-DL2019, PRP is only inferior to the GPT-4 solution on the NDCG@5 and NDCG@10 metrics, while outperforming other existing solutions, such as InstructGPT which has 175B parameters, by over 10% for nearly all ranking metrics.

Learning to Rank when Grades Matter

no code implementations14 Jun 2023 Le Yan, Zhen Qin, Gil Shamir, Dong Lin, Xuanhui Wang, Mike Bendersky

In this paper, we conduct a rigorous study of learning to rank with grades, where both ranking performance and grade prediction performance are important.


LibAUC: A Deep Learning Library for X-Risk Optimization

1 code implementation5 Jun 2023 Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang

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-risks.

Benchmarking Classification +2

Query Expansion by Prompting Large Language Models

no code implementations5 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.

Metric-agnostic Ranking Optimization

no code implementations17 Apr 2023 Qingyao Ai, Xuanhui Wang, Michael Bendersky

To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in \textit{Metric-agnostic Ranking Optimization}.

Information Retrieval Learning-To-Rank +2

Towards Disentangling Relevance and Bias in Unbiased Learning to Rank

no code implementations28 Dec 2022 Yunan Zhang, Le Yan, Zhen Qin, Honglei Zhuang, Jiaming Shen, Xuanhui Wang, Michael Bendersky, Marc Najork

We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.


Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance

no code implementations2 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.

Learning-To-Rank regression

RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses

no code implementations12 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.

Rank4Class: A Ranking Formulation for Multiclass Classification

no code implementations17 Dec 2021 Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes.

Classification Image Classification +4

Improving Neural Ranking via Lossless Knowledge Distillation

no code implementations30 Sep 2021 Zhen Qin, Le Yan, Yi Tay, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized identically to their teachers.

Knowledge Distillation Learning-To-Rank

Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank

no code implementations29 Sep 2021 Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.

Image Classification Information Retrieval +4

Distilling Interpretable Models into Human-Readable Code

1 code implementation21 Jan 2021 Walker Ravina, Ethan Sterling, Olexiy Oryeshko, Nathan Bell, Honglei Zhuang, Xuanhui Wang, Yonghui Wu, Alexander Grushetsky

The goal of model distillation is to faithfully transfer teacher model knowledge to a model which is faster, more generalizable, more interpretable, or possesses other desirable characteristics.

Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees

no code implementations ICLR 2021 Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.


Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction

no code implementations18 May 2020 Nan Wang, Zhen Qin, Xuanhui Wang, Hongning Wang

Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback.


Learning-to-Rank with BERT in TF-Ranking

no code implementations17 Apr 2020 Shuguang Han, Xuanhui Wang, Mike Bendersky, Marc Najork

This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.

Document Ranking Learning-To-Rank +2

Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking

no code implementations21 Oct 2019 Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.

Information Retrieval Learning-To-Rank +1

Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

2 code implementations11 Nov 2018 Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork

To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.


Multi-Faceted Ranking of News Articles using Post-Read Actions

no code implementations3 May 2012 Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang

Our findings show that it is possible to incorporate post-read signals that are commonly available on online news sites to improve quality of recommendations.

Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

4 code implementations31 Mar 2010 Lihong Li, Wei Chu, John Langford, Xuanhui Wang

\emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature.

News Recommendation Recommendation Systems

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