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Cong Liu

Cong Liu has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Low-resource languages; often with Haifeng Sun, Jianxin Liao, Jiaxing Liu.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 14 · 56 works · 507 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 7 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10133

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

🎓Hubei University of TechnologyWuhan · China2015–2025 · 17 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2025 · 1 papercurrent🎓University of Shanghai for Science and TechnologyShanghai · China2015–2023 · 8 papers🎓The University of Texas at DallasRichardson · United States2014–2020 · 5 papers🎓Sun Yat-sen UniversityGuangzhou · China2005–2023 · 3 papers🎓Harbin Engineering UniversityHarbin · China2021–2024 · 2 papers🎓Baoji University of Arts and SciencesXi'an · China2016–2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 13 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.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksLow-resource languages · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Cool-Fusion: Fuse Large Language Models without Training (2025 · 8 citations)
    Cool-Fusion addresses how to combine several heterogeneous LLMs so they benefit from each other's strengths.
  2. ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026)
    Running large language models on modest hardware is costly. Current customization routes either compress and fine-tune separately for every target task, which is expensive, or tune once on general data, which tends to hurt task-level quality through interference and forgetting. ModularMoE addresses this by turning an…

All works

3 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. Deep Hashing with Minimal-Distance-Separated Hash Centers ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetLiangdao Wang et al.2023 · 71 citations
  2. Cool-Fusion: Fuse Large Language Models without TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Cong Liu, Xiaojun Quan, Yan Pan, et al.2025 · 8 citations
  3. ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource SettingsFindings of the Association for Computational LinguisticsJiaxing Liu, Qi Qi, Haifeng Sun, et al.2026

 Co-authors here

8 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 7 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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