🧑‍🔬 Zonghan Yang

🏛️ Affiliations

🎓Shanghai Jiao Tong UniversityShanghai · China2022–2026 · 5 papersEmployment since 2022 · PhD Student, Department of Computer Science and Engineeringcurrent🎓Tsinghua UniversityBeijing · China2018–2025 · 16 papers🔬Beijing National Research Center for Information Science and TechnologyBeijing · China2018–2023 · 5 papers🔬State Key Laboratory of Intelligent Technology and SystemsBeijing · China2018–2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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. Parameter-efficient fine-tuning of large-scale pre-trained language models (2023 · 1,325 citations)
   Full fine-tuning of large pre-trained language models (PLMs) is costly: every parameter is updated and each downstream task needs its own complete copy of the model.
2. Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025 · 11 citations)
   Large multi-modal models such as GPT-4V still struggle with tasks where they must relate several pieces of visual information and express that reasoning accurately in text.
3. Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents (2025 · 4 citations)
   Automated GitHub issue fixing, as measured by SWE-bench, has split into two camps. Multi-turn SWE-Agents are flexible but hard to train end-to-end. Pipeline-style Agentless methods are easy to optimize with verifiable rewards but rigid. The authors argue the two approaches can be combined: training on Agentless…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Parameter-efficient fine-tuning of large-scale pre-trained language modelsNature Machine IntelligenceNing Ding, Yujia Qin, Guang You Yang, et al.2023 · 1,325 citations
2. Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetZonghan Yang et al.2019 · 104 citations
3. Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal ModelsInternational Conference on Computational Linguistics (COLING)Xuanyu Lei, Zonghan Yang, Xinrui Chen, et al.2025 · 11 citations
4. Kimi-Dev: Agentless Training as Skill Prior for SWE-AgentsarXiv:2509.23045Zonghan Yang, Shengjie Wang, Kelin Fu, et al.2025 · 4 citations

🤝 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

• Bofei Gao
• Chi-Min Chan
• Ning Ding
• Peng Li
• Shengding Hu
• Weilin Zhao
• Weimin Xiong
• Weize Chen
• Xuanyu Lei
• Yang Liu
• Yibo Miao
• Yingwei Ma

Venues

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

• International Conference on Computational Linguistics (COLING): COLING 2025
• Nature Machine Intelligence: Nature Machine Intelligence 2023
• Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

🔗 Cited by

Papers of our library cite their works 619 times (of the 149,439 papers whose bibliographies we read); 165 citing papers with a page here →.

Updated 30 October 2026

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