Xuanhui Wang
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Michael Bendersky
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
Michael Bendersky
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
Michael Bendersky
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) (2024)
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Jianmo Ni
Mike Bendersky
Proc. of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2023)
RD-Suite: A Benchmark for Ranking Distillation
He Zhang
37th Conference on Neural Information Processing Systems (NeurIPS) (2023)
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
Pratyush Kar
Bing-Rong Lin
Michael Bendersky
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (2023)
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Andrew Drozdov
Zhuyun Dai
Razieh Negin Rahimi
Andrew McCallum
Mohit Iyyer
EMNLP 2023 (Findings)
Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian
Hamed Zamani
Han Zhao
Michael Bendersky
37th Conference on Neural Information Processing Systems (NeurIPS 2023)
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Yunan Zhang
Mike Bendersky
29TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2023)
