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Jialu Liu

Jialu Liu has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Tianqi Liu, Donald Metzler, Jiaming Shen.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 16 · 42 works · 2,075 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 237 citations in our library · ⭐ Paper Star rank #3751

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Central South UniversityChangsha · China2023–2026 · 3 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2024–2026 · 2 paperscurrent🔬Institute of AutomationBeijing · China2024–2026 · 2 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2024–2026 · 2 paperscurrent🎓Zhejiang UniversityHangzhou · China2010–2026 · 2 paperscurrent🔬State Key Laboratory of Silicon MaterialsHangzhou2026 · 1 papercurrent🏢GoogleMountain View · United States2016–2024 · 18 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2013–2016 · 5 papers🎓Tianjin UniversityTianjin · China2011–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5 Arts and Humanities · 2 works Computer Science · 2 works Artificial Intelligence · 2 works Language and Linguistics · 2 works Information Systems · 1 work

🧭 Topics 1 Language models & pretraining · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024 · 297 citations)
    The paper addresses why off-the-shelf LLMs have struggled to rank documents for a query as well as fine-tuned rankers.
  2. Statistical Rejection Sampling Improves Preference Optimization (2023 · 101 citations)
    The paper addresses a statistical gap in offline alignment methods. DPO and SLiC learn from preference pairs, but those pairs come from human-annotated data or the SFT policy, not from the optimal policy being estimated. As a result, their fitting is not a proper maximum-likelihood estimate. The authors propose…

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 237 times (of the 141,840 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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