🧑‍🔬 Xuanhui Wang

🏛️ Affiliations

🏢GoogleMountain View · United States2016–2026 · 55 paperscurrent🎓Sun Yat-sen UniversityGuangzhou · China2026 · 1 papercurrent🏥The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou · China2026 · 1 papercurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2004–2010 · 25 papers🏢YahooSunnyvale · United States2010–2015 · 11 papers🏢MetaMenlo Park · United States2012–2013 · 7 papers🎓Qingdao Agricultural UniversityQingdao · China2018–2025 · 5 papers🎓China University of Mining and TechnologyXuzhou · China2020–2025 · 3 papers🎓Shanxi Institute of TechnologyYangquan · China2019–2022 · 3 papers🎓University of Massachusetts AmherstAmherst Center · United States2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024 · 300 citations)
   The paper addresses why off-the-shelf LLMs have struggled to rank documents for a query as well as fine-tuned rankers.
2. LiPO: Listwise Preference Optimization through Learning-to-Rank (2025 · 31 citations)
   Most alignment methods that skip full RLHF, such as DPO and SLiC, learn only from pairs of responses. Human or reward-model feedback, however, often ranks several responses to the same prompt. The authors treat alignment as a Learning-to-Rank (LTR) problem. The prompt plays the role of a query, the sampled responses…

All works

5 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Large Language Models are Effective Text Rankers with Pairwise Ranking PromptingFindings of the Association for Computational LinguisticsZhen Qin, Rolf Jagerman, Kai Hui, et al.2024 · 300 citations
2. Probabilistic dyadic data analysis with local and global consistency ↗International Conference on Machine Learning · no summary yetDeng Cai, Xuanhui Wang, Xiaofei He2009 · 177 citations
3. Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees ↗International Conference on Learning Representations · no summary yetZhen Qin et al.2021 · 56 citations
4. LiPO: Listwise Preference Optimization through Learning-to-RankConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Tianqi Liu, Zhen Qin, Junru Wu, et al.2025 · 31 citations
5. Born Again Neural Rankers. ↗arXiv (Cornell University) · no summary yetZhen Qin et al.2021 · 4 citations

🤝 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

• Jialu Liu ×2
• Jiaming Shen ×2
• Junru Wu ×2
• Tianqi Liu ×2
• Zhen Qin ×2
• Donald Metzler
• Kai Hui
• Michael Bendersky
• Misha Khalman
• Mohammad Saleh
• Peter J Liu
• Rishabh Joshi

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2025
• International Conference on Learning Representations
• International Conference on Machine Learning

🔗 Cited by

Papers of our library cite their works 188 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

Updated 30 October 2026

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