Yujia Qin

2.3k total citations · 1 hit paper
41 papers, 1.0k citations indexed

About

Yujia Qin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Materials Chemistry. According to data from OpenAlex, Yujia Qin has authored 41 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Materials Chemistry. Recurrent topics in Yujia Qin's work include Topic Modeling (22 papers), Natural Language Processing Techniques (20 papers) and Crystallization and Solubility Studies (7 papers). Yujia Qin is often cited by papers focused on Topic Modeling (22 papers), Natural Language Processing Techniques (20 papers) and Crystallization and Solubility Studies (7 papers). Yujia Qin collaborates with scholars based in China, United States and United Kingdom. Yujia Qin's co-authors include Zhiyuan Liu, Maosong Sun, Junbo Gong, Yusheng Su, Shengding Hu, Shichao Du, Xiaozhi Wang, Juanzi Li, Chi-Min Chan and Ning Ding and has published in prestigious journals such as Scientific Reports, Journal of Controlled Release and Nanoscale.

In The Last Decade

Yujia Qin

37 papers receiving 994 citations

Hit Papers

Parameter-efficient fine-tuning of large-scale pre-traine... 2023 2026 2024 2025 2023 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Parameter-efficient fine-tuning of large-scale pre-trained language models
    2023 420 citations Ning Ding, Yujia Qin et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yujia Qin China 16 425 256 129 127 115 41 1.0k
Xiaolu Zhang China 18 323 0.8× 85 0.3× 156 1.2× 44 0.3× 31 0.3× 100 1.1k
Siegfried Benkner Austria 17 134 0.3× 24 0.1× 54 0.4× 25 0.2× 15 0.1× 91 955
S. Siva Sathya India 11 226 0.5× 62 0.2× 59 0.5× 4 0.0× 6 0.1× 44 771
Sean B. Holden United Kingdom 13 243 0.6× 83 0.3× 92 0.7× 131 1.1× 26 1.1k
Shuangjia Zheng China 22 655 1.5× 541 2.1× 137 1.1× 31 0.3× 46 2.3k
Zhuo Chen China 17 1.0k 2.5× 132 0.5× 174 1.3× 17 0.1× 66 1.9k
Abhinav Vishnu United States 19 202 0.5× 302 1.2× 107 0.8× 40 0.3× 71 1.9k
Zhen Li China 24 312 0.7× 238 0.9× 761 5.9× 25 0.2× 109 1.9k

Countries citing papers authored by Yujia Qin

Since Specialization
Citations

This map shows the geographic impact of Yujia Qin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yujia Qin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yujia Qin more than expected).

Fields of papers citing papers by Yujia Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yujia Qin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yujia Qin. The network helps show where Yujia Qin may publish in the future.

Co-authorship network of co-authors of Yujia Qin

This figure shows the co-authorship network connecting the top 25 collaborators of Yujia Qin. A scholar is included among the top collaborators of Yujia Qin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yujia Qin. Yujia Qin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Januskaite, Patricija, et al.. (2025). 3D printed sex-specific medicines: Excipient-mediated modulation boosts systemic drug exposure by more than three-fold in male rats. Journal of Controlled Release. 390. 114577–114577.
2.
Qian, Cheng, Yujia Qin, Xin Cong, et al.. (2024). Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents. 1088–1113. 8 indexed citations
3.
Qin, Yujia, Xiaozhi Wang, Yusheng Su, et al.. (2024). Exploring Universal Intrinsic Task Subspace for Few-Shot Learning via Prompt Tuning. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 3631–3643. 2 indexed citations
4.
Feng, Xueyang, Zhiyuan Chen, Yujia Qin, et al.. (2024). Large Language Model-based Human-Agent Collaboration for Complex Task Solving. 1336–1357. 6 indexed citations
5.
Qin, Yujia, et al.. (2024). Empowering Large Language Models: Tool Learning for Real-World Interaction. 2983–2986. 1 indexed citations
6.
Ye, Yining, Yujia Qin, Xin Cong, et al.. (2024). DebugBench: Evaluating Debugging Capability of Large Language Models. 4173–4198. 16 indexed citations
7.
Ding, Ning, et al.. (2023). Parameter-efficient Weight Ensembling Facilitates Task-level Knowledge Transfer. 270–282. 2 indexed citations
8.
Ding, Ning, Yujia Qin, Guang Yang, et al.. (2023). Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence. 5(3). 220–235. 420 indexed citations breakdown →
9.
Qin, Yujia, Cheng Qian, Xu Han, et al.. (2023). Recyclable Tuning for Continual Pre-training. 11403–11426. 3 indexed citations
10.
Cui, Ganqu, Yujia Qin, Chong Fu, et al.. (2023). Removing Backdoors in Pre-trained Models by Regularized Continual Pre-training. Transactions of the Association for Computational Linguistics. 11. 1608–1623.
11.
Ding, Ning, Yulin Chen, Bokai Xu, et al.. (2023). Enhancing Chat Language Models by Scaling High-quality Instructional Conversations. 3029–3051. 39 indexed citations
12.
Zhang, Hongying, et al.. (2023). Comparing Three Methods of Selecting Training Samples in Supervised Classification of Multispectral Remote Sensing Images. Sensors. 23(20). 8530–8530. 9 indexed citations
13.
Ong, Jun Jie, Simon Gaisford, Michael T. Cook, et al.. (2023). Supramolecular chemistry enables vat photopolymerization 3D printing of novel water-soluble tablets. International Journal of Pharmaceutics. 643. 123286–123286. 15 indexed citations
14.
Qin, Yujia, Lan Yan, Yankai Lin, et al.. (2023). WebCPM: Interactive Web Search for Chinese Long-form Question Answering. 8968–8988. 14 indexed citations
15.
Huang, Yufei, Yujia Qin, Yichun Yin, et al.. (2022). FPT: Improving Prompt Tuning Efficiency via Progressive Training. 6877–6887. 2 indexed citations
16.
Su, Yusheng, Xiaozhi Wang, Yujia Qin, et al.. (2022). On Transferability of Prompt Tuning for Natural Language Processing. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3949–3969. 37 indexed citations
17.
Qin, Yujia, Yankai Lin, Jiajie Zhang, et al.. (2022). Knowledge Inheritance for Pre-trained Language Models. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3921–3937. 18 indexed citations
18.
Qin, Yujia, Fanchao Qi, Yangdong Deng, et al.. (2022). Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 5060–5072. 1 indexed citations
19.
Qin, Yujia, Cheng Qian, Jing Yi, et al.. (2022). Exploring Mode Connectivity for Pre-trained Language Models. 6726–6746. 3 indexed citations
20.
Wang, Ziqi, Yujia Qin, Wenxuan Zhou, et al.. (2019). Learning from Explanations with Neural Execution Tree. arXiv (Cornell University). 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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