Relevance weighting using within-document term statistics. - Kai Hui 001; Ben He; Tiejian Luo; Bin Wang 004 (CIKM, 2011)

A Comparative Study of Pseudo Relevance Feedback for Ad-hoc Retrieval. - Kai Hui 001; Ben He; Tiejian Luo; Bin Wang 004 (ICTIR, 2011)

GUCAS at TREC 2011 Microblog Track. - Xin Zhang 073; Kai Hui 001; Ben He; Tiejian Luo (TREC, 2011)

Sponsored Search Ad Selection by Keyword Structure Analysis. - Kai Hui 001; Bin Gao 001; Ben He; Tiejian Luo (ECIR, 2013)

On Evaluating Query Performance Predictors. - Yu Huang; Tiejian Luo; Xiang Wang; Kai Hui 001; Wengjie Wang; Ben He (ICPCA/SWS, 2013)

Towards Robust & Reusable Evaluation for Novelty & Diversity. - Kai Hui 001 (PIKM@CIKM, 2014)

Selective Labeling and Incomplete Label Mitigation for Low-Cost Evaluation. - Kai Hui 001; Klaus Berberich (SPIRE, 2015)

Cluster Hypothesis in Low-Cost IR Evaluation with Different Document Representations. - Kai Hui 001; Klaus Berberich (WWW (Companion Volume), 2016)

A document-based neural relevance model for effective clinical decision support. - Yanhua Ran; Ben He; Kai Hui 001; Jungang Xu; Le Sun 001 (BIBM, 2017)

Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing. - Kai Hui 001; Klaus Berberich (ECIR, 2017)

Low-Cost Preference Judgment via Ties. - Kai Hui 001; Klaus Berberich (ECIR, 2017)

PACRR: A Position-Aware Neural IR Model for Relevance Matching. - Kai Hui 001; Andrew Yates; Klaus Berberich; Gerard de Melo (EMNLP, 2017)

Merge-Tie-Judge: Low-Cost Preference Judgments with Ties. - Kai Hui 001; Klaus Berberich (ICTIR, 2017)

Dealing with Incomplete Judgments in Cascade Measures. - Kai Hui 001; Klaus Berberich; Ida Mele (ICTIR, 2017)

Contextualized PACRR for Complex Answer Retrieval. - Sean MacAvaney; Andrew Yates; Kai Hui 001 (TREC, 2017)

Position-Aware Representations for Relevance Matching in Neural Information Retrieval. - Kai Hui 001; Andrew Yates; Klaus Berberich; Gerard de Melo (WWW (Companion Volume), 2017)

A Position-Aware Deep Model for Relevance Matching in Information Retrieval. - Kai Hui 001; Andrew Yates; Klaus Berberich; Gerard de Melo (CoRR, 2017)

RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model. - Kai Hui 001; Andrew Yates; Klaus Berberich; Gerard de Melo (CoRR, 2017)

An Approach for Weakly-Supervised Deep Information Retrieval. - Sean MacAvaney; Kai Hui 001; Andrew Yates (CoRR, 2017)

DE-PACRR: Exploring Layers Inside the PACRR Model. - Andrew Yates; Kai Hui 001 (CoRR, 2017)

Automatic methods for low-cost evaluation and position-aware models for neural information retrieval. - Kai Hui 001 (2017)

TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring. - Cancan Jin; Ben He; Kai Hui 001; Le Sun 001 (ACL (1), 2018)

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval. - Canjia Li; Yingfei Sun; Ben He; Le Wang; Kai Hui 001; Andrew Yates; Le Sun 001; Jungang Xu (EMNLP, 2018)

Characterizing Question Facets for Complex Answer Retrieval. - Sean MacAvaney; Andrew Yates; Arman Cohan; Luca Soldaini; Kai Hui 001; Nazli Goharian; Ophir Frieder (SIGIR, 2018)

Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval. - Kai Hui 001; Andrew Yates; Klaus Berberich; Gerard de Melo (WSDM, 2018)

Neural relevance model using similarities with elite documents for effective clinical decision support. - Yanhua Ran; Ben He; Kai Hui 001; Jungang Xu; Le Sun 001 (Int. J. Data Min. Bioinform., 2018)

Content-Based Weak Supervision for Ad-Hoc Re-Ranking. - Sean MacAvaney; Andrew Yates; Kai Hui 001; Ophir Frieder (SIGIR, 2019)

Overcoming low-utility facets for complex answer retrieval. - Sean MacAvaney; Andrew Yates; Arman Cohan; Luca Soldaini; Kai Hui 001; Nazli Goharian; Ophir Frieder (Inf. Retr. J., 2019)

BERT-QE: Contextualized Query Expansion for Document Re-ranking. - Zhi Zheng; Kai Hui 001; Ben He; Xianpei Han; Le Sun 001; Andrew Yates (EMNLP (Findings), 2020)

Question Answering using Web Lists. - Anoop R. Katti; Kai Hui 001; Adrià de Gispert; Hagen Fürstenau (CIKM, 2021)

Simplified TinyBERT: Knowledge Distillation for Document Retrieval. - Xuanang Chen; Ben He; Kai Hui 001; Le Sun 001; Yingfei Sun (ECIR (2), 2021)

Contextualized Offline Relevance Weighting for Efficient and Effective Neural Retrieval. - Xuanang Chen; Ben He; Kai Hui 001; Yiran Wang; Le Sun 001; Yingfei Sun (SIGIR, 2021)

Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context. - Xiaoyang Chen; Kai Hui 001; Ben He; Xianpei Han; Le Sun 001; Zheng Ye (CoRR, 2021)

Contextualized query expansion via unsupervised chunk selection for text retrieval. - Zhi Zheng; Kai Hui 001; Ben He; Xianpei Han; Le Sun 001; Andrew Yates (Inf. Process. Manag., 2021)

ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference. - Kai Hui 001; Honglei Zhuang; Tao Chen 008; Zhen Qin 001; Jing Lu; Dara Bahri; Ji Ma 004; Jai Prakash Gupta 001; Cícero Nogueira dos Santos; Yi Tay; Donald Metzler (ACL (Findings), 2022)

Incorporating Ranking Context for End-to-End BERT Re-ranking. - Xiaoyang Chen; Kai Hui 001; Ben He; Xianpei Han; Le Sun 001; Zheng Ye (ECIR (1), 2022)

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning. - Vamsi Aribandi; Yi Tay; Tal Schuster; Jinfeng Rao; Huaixiu Steven Zheng; Sanket Vaibhav Mehta; Honglei Zhuang; Vinh Q. Tran 002; Dara Bahri; Jianmo Ni; Jai Prakash Gupta 001; Kai Hui 001; et al. (ICLR, 2022)

Transformer Memory as a Differentiable Search Index. - Yi Tay; Vinh Tran 002; Mostafa Dehghani 001; Jianmo Ni; Dara Bahri; Harsh Mehta; Zhen Qin 001; Kai Hui 001; Zhe Zhao 001; Jai Prakash Gupta 001; Tal Schuster; William W. Cohen; et al. (NeurIPS, 2022)

Retrieval Augmentation for T5 Re-ranker using External Sources. - Kai Hui 001; Tao Chen 008; Zhen Qin 001; Honglei Zhuang; Fernando Diaz 001; Mike Bendersky; Don Metzler (CoRR, 2022)

Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models. - Bernd Bohnet; Vinh Q. Tran 002; Pat Verga; Roee Aharoni; Daniel Andor; Livio Baldini Soares; Jacob Eisenstein; Kuzman Ganchev; Jonathan Herzig; Kai Hui 001; Tom Kwiatkowski; Ji Ma 004; et al. (CoRR, 2022)

PaRaDe: Passage Ranking using Demonstrations with LLMs. - Andrew Drozdov; Honglei Zhuang; Zhuyun Dai; Zhen Qin 001; Razieh Rahimi; Xuanhui Wang; Dana Alon; Mohit Iyyer; Andrew McCallum; Donald Metzler; Kai Hui 001 (EMNLP (Findings), 2023)

How Does Generative Retrieval Scale to Millions of Passages? - Ronak Pradeep; Kai Hui 001; Jai Gupta 001; Ádám D. Lelkes; Honglei Zhuang; Jimmy Lin; Donald Metzler; Vinh Q. Tran 002 (EMNLP, 2023)

RD-Suite: A Benchmark for Ranking Distillation. - Zhen Qin 001; Rolf Jagerman; Rama Kumar Pasumarthi; Honglei Zhuang; He Zhang; Aijun Bai; Kai Hui 001; Le Yan; Xuanhui Wang (NeurIPS, 2023)

Learning List-Level Domain-Invariant Representations for Ranking. - Ruicheng Xian; Honglei Zhuang; Zhen Qin 001; Hamed Zamani; Jing Lu; Ji Ma 004; Kai Hui 001; Han Zhao 002; Xuanhui Wang; Michael Bendersky (NeurIPS, 2023)

RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. - Honglei Zhuang; Zhen Qin 001; Rolf Jagerman; Kai Hui 001; Ji Ma 004; Jing Lu; Jianmo Ni; Xuanhui Wang; Michael Bendersky (SIGIR, 2023)

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. - Zhen Qin 001; Rolf Jagerman; Kai Hui 001; Honglei Zhuang; Junru Wu; Jiaming Shen; Tianqi Liu 002; Jialu Liu; Donald Metzler; Xuanhui Wang; Michael Bendersky (CoRR, 2023)

PaRaDe: Passage Ranking using Demonstrations with Large Language Models. - Andrew Drozdov; Honglei Zhuang; Zhuyun Dai; Zhen Qin 001; Razieh Rahimi; Xuanhui Wang; Dana Alon; Mohit Iyyer; Andrew McCallum; Donald Metzler; Kai Hui 001 (CoRR, 2023)

Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers. - Minghan Li 002; Honglei Zhuang; Kai Hui 001; Zhen Qin 001; Jimmy Lin; Rolf Jagerman; Xuanhui Wang; Michael Bendersky (CoRR, 2023)

Dealing with textual noise for robust and effective BERT re-ranking. - Xuanang Chen; Ben He; Kai Hui 001; Le Sun 001; Yingfei Sun (Inf. Process. Manag., 2023)

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. - Zhen Qin 001; Rolf Jagerman; Kai Hui 001; Honglei Zhuang; Junru Wu; Le Yan; Jiaming Shen; Tianqi Liu 002; Jialu Liu; Donald Metzler; Xuanhui Wang; Michael Bendersky (NAACL-HLT (Findings), 2024)

Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels. - Honglei Zhuang; Zhen Qin 001; Kai Hui 001; Junru Wu; Le Yan; Xuanhui Wang; Michael Bendersky (NAACL (Short Papers), 2024)

Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers? - Minghan Li 002; Honglei Zhuang; Kai Hui 001; Zhen Qin 001; Jimmy Lin; Rolf Jagerman; Xuanhui Wang; Michael Bendersky (SIGIR, 2024)

Gecko: Versatile Text Embeddings Distilled from Large Language Models. - Jinhyuk Lee; Zhuyun Dai; Xiaoqi Ren; Blair Chen; Daniel Cer; Jeremy R. Cole; Kai Hui 001; Michael Boratko; Rajvi Kapadia; Wen Ding; Yi Luan; Sai Meher Karthik Duddu; et al. (CoRR, 2024)

Inference Scaling for Long-Context Retrieval Augmented Generation. - Zhenrui Yue; Honglei Zhuang; Aijun Bai; Kai Hui 001; Rolf Jagerman; Hansi Zeng; Zhen Qin 001; Dong Wang 002; Xuanhui Wang; Michael Bendersky (ICLR, 2025)

Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs? - Hansi Zeng; Kai Hui 001; Honglei Zhuang; Zhen Qin 001; Zhenrui Yue; Hamed Zamani; Dana Alon (CoRR, 2025)