Kai Hui
Kai is a Senior Software Engineer at Google AI, working on IR and NLP. Prior to that, he worked at Amazon Alexa and SAP. Kai took his Ph.D. from Max-Planck Institute for Informatics in Germany, where he worked on neural IR models and IR evaluation. His research interests focus on the developments of deep learning models for ad-hoc information retrieval and question answering. Kai co-authored peer-reviewed research papers and serves as program committee members, editorial board members, and reviewers for IR/NLP conferences and journals. For complete list of his publications, please check out his homepage or visit his Google Scholar page.
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) (2024)
Beyond Yes and No: Improving Zero-Shot Pointwise LLM Rankers via Scoring Fine-Grained Relevance Labels
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
Minghan Li
Jimmy Lin
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) (2024)
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Andrew Drozdov
Zhuyun Dai
Razieh Negin Rahimi
Andrew McCallum
Mohit Iyyer
EMNLP 2023 (Findings)
RD-Suite: A Benchmark for Ranking Distillation
He Zhang
37th Conference on Neural Information Processing Systems (NeurIPS) (2023)
Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian
Hamed Zamani
Han Zhao
37th Conference on Neural Information Processing Systems (NeurIPS 2023)
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Jianmo Ni
Proc. of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2023)
Understanding Generative Retrieval at Scale
Ronak Pradeep
Jimmy Lin
EMNLP 2023
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Pat Verga
Jianmo Ni
arXiv (2022)
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
Tao Chen
Cicero Nogueira dos Santos
Yi Tay
ACL: Findings 2022 (2022)
