Qingxiu Dong

1.0k total citations · 1 hit paper
11 papers, 297 citations indexed

About

Qingxiu Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Qingxiu Dong has authored 11 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 1 paper in Information Systems. Recurrent topics in Qingxiu Dong's work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Multimodal Machine Learning Applications (5 papers). Qingxiu Dong is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Multimodal Machine Learning Applications (5 papers). Qingxiu Dong collaborates with scholars based in China, United States and Hong Kong. Qingxiu Dong's co-authors include Jingjing Xu, Zhifang Sui, Ce Zheng, Baobao Chang, Zhiyong Wu, Heming Xia, Damai Dai, Xu Sun, Rui Li and Jingyuan Ma and has published in prestigious journals such as Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Qingxiu Dong

10 papers receiving 283 citations

Hit Papers

A Survey on In-context Learning 2024 2026 2025 2024 50 100 150
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. A Survey on In-context Learning
    2024 173 citations Qingxiu Dong, Lei Li et al. profile →

Peers

Qingxiu Dong
Lukas Gianinazzi Switzerland
Yeganeh Kordi United States
Hengyi Cai China
Quentin Anthony United States
Shaochen Zhong United States
Ben Coppin United Kingdom
Hongye Jin United States
Le Sun China
Christopher A. Choquette-Choo United States
Mohamed Yehia Dahab Saudi Arabia
Lukas Gianinazzi Switzerland
Qingxiu Dong
Citations per year, relative to Qingxiu Dong Qingxiu Dong (= 1×) peers Lukas Gianinazzi

Countries citing papers authored by Qingxiu Dong

Since Specialization
Citations

This map shows the geographic impact of Qingxiu Dong'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 Qingxiu Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingxiu Dong more than expected).

Fields of papers citing papers by Qingxiu Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qingxiu Dong. 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 Qingxiu Dong. The network helps show where Qingxiu Dong may publish in the future.

Co-authorship network of co-authors of Qingxiu Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Qingxiu Dong. A scholar is included among the top collaborators of Qingxiu Dong 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 Qingxiu Dong. Qingxiu Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Li, Zheng, et al.. (2024). Can Large Multimodal Models Uncover Deep Semantics Behind Images?. 1898–1912. 3 indexed citations
2.
Zhu, Wenhao, Qingxiu Dong, Jingjing Xu, et al.. (2024). Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis. 2765–2781. 45 indexed citations
3.
Dong, Qingxiu, et al.. (2024). Statistical dataset evaluation: A case study on named entity recognition. 31(1). 90–110.
4.
Xia, Heming, Qingxiu Dong, Peiyi Wang, et al.. (2024). Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding. 7655–7671. 13 indexed citations
5.
Dong, Qingxiu, Lei Li, Damai Dai, et al.. (2024). A Survey on In-context Learning. 1107–1128. 173 indexed citations breakdown →
6.
Xu, Jingjing, Qingxiu Dong, Ce Zheng, et al.. (2023). Can Language Models Understand Physical Concepts?. 10 indexed citations
7.
Zheng, Ce, et al.. (2023). Can We Edit Factual Knowledge by In-Context Learning?. 4862–4876. 15 indexed citations
8.
Xia, Heming, et al.. (2023). ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories. 2009–2026. 1 indexed citations
9.
Dong, Qingxiu, Damai Dai, Yifan Song, et al.. (2022). Calibrating Factual Knowledge in Pretrained Language Models. 5937–5947. 17 indexed citations
10.
Dong, Qingxiu, Ziwei Qin, Heming Xia, et al.. (2022). Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 932–946. 5 indexed citations
11.
Dong, Qingxiu, Xiaojun Wan, & Yue Cao. (2021). ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation. 424–434. 15 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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