Elamur Research⌕
☰
home / authors / Conghui Zhu

Conghui Zhu

Conghui Zhu has 2 papers in our library, in 2025, most of them on Efficient models & compression; often with Muyun Yang, Tiejun Zhao, Bing Xu.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 13 · 80 works · 619 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 49 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #9358

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

🎓Harbin Institute of TechnologyHarbin · China2009–2026 · 54 paperscurrent🎓Beihang UniversityBeijing · China2019–2022 · 4 papers🎓Zhejiang Wanli UniversityNingbo · China2022 · 2 papers🏢BaiduBeijing · China2013–2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025 · 12 citations)
    LoRA lowers the cost of fine-tuning. For billion-parameter models, though, its trainable weights still need considerable compute. Storage also adds up when providers keep a separate adapter for each user. Earlier pruning methods (Sparse Adapter, AdaLoRA, SoRA) rank adapter importance using only properties of the…
  2. MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025 · 4 citations)
    Large language models often fail on instructions that combine many constraints, frequently dropping some of them.

All works

4 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. CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection ↗Findings of the Association for Computational Linguistics: NAACL 2022 · no summary yetZhen Li et al.2022 · 128 citations
  2. Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetGuanhua Zhang et al.2020 · 81 citations
  3. LoRA-drop: Efficient LoRA Parameter Pruning based on Output EvaluationInternational Conference on Computational Linguistics (COLING)Hongyun Zhou, Xiangyu Lu, Wang Xu, et al.2025 · 12 citations
  4. MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Hui Huang, Jiaheng Liu, Yancheng He, et al.2025 · 4 citations

 Co-authors here

7 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 49 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/conghui-zhu/